awesome_3dgs_papers.yaml

- id: lin2025diffsplat
  title: 'DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat
    Generation'
  authors: Chenguo Lin, Panwang Pan, Bangbang Yang, Zeming Li, Yadong Mu
  year: '2025'
  abstract: 'Recent advancements in 3D content generation from text or a single image
    struggle with limited high-quality 3D datasets and inconsistency from 2D multi-view
    generation. We introduce DiffSplat, a novel 3D generative framework that natively
    generates 3D Gaussian splats by taming large-scale text-to-image diffusion models.
    It differs from previous 3D generative models by effectively utilizing web-scale
    2D priors while maintaining 3D consistency in a unified model. To bootstrap the
    training, a lightweight reconstruction model is proposed to instantly produce
    multi-view Gaussian splat grids for scalable dataset curation. In conjunction
    with the regular diffusion loss on these grids, a 3D rendering loss is introduced
    to facilitate 3D coherence across arbitrary views. The compatibility with image
    diffusion models enables seamless adaptions of numerous techniques for image generation
    to the 3D realm. Extensive experiments reveal the superiority of DiffSplat in
    text- and image-conditioned generation tasks and downstream applications. Thorough
    ablation studies validate the efficacy of each critical design choice and provide
    insights into the underlying mechanism.

    '
  project_page: https://chenguolin.github.io/projects/DiffSplat/
  paper: https://arxiv.org/pdf/2501.16764.pdf
  code: https://github.com/chenguolin/DiffSplat
  video: null
  tags:
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/lin2025diffsplat.jpg
  publication_date: '2025-01-28T07:38:59+00:00'
  date_source: arxiv
- id: armagan2025trickgs
  title: 'Trick-GS: A Balanced Bag of Tricks for Efficient Gaussian Splatting'
  authors: Anil Armagan, Albert Saà-Garriga, Bruno Manganelli, Mateusz Nowak, Mehmet
    Kerim Yucel
  year: '2025'
  abstract: 'Gaussian splatting (GS) for 3D reconstruction has become quite popular
    due to their fast training, inference speeds and high quality reconstruction.
    However, GS-based reconstructions generally consist of millions of Gaussians,
    which makes them hard to use on computationally constrained devices such as smartphones.
    In this paper, we first propose a principled analysis of advances in efficient
    GS methods. Then, we propose Trick-GS, which is a careful combination of several
    strategies including (1) progressive training with resolution, noise and Gaussian
    scales, (2) learning to prune and mask primitives and SH bands by their significance,
    and (3) accelerated GS training framework. Trick-GS takes a large step towards
    resource-constrained GS, where faster run-time, smaller and faster-convergence
    of models is of paramount concern. Our results on three datasets show that Trick-GS
    achieves up to 2x faster training, 40x smaller disk size and 2x faster rendering
    speed compared to vanilla GS, while having comparable accuracy.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.14534.pdf
  code: null
  video: null
  tags:
  - Acceleration
  thumbnail: assets/thumbnails/armagan2025trickgs.jpg
  publication_date: '2025-01-24T14:40:40+00:00'
  date_source: arxiv
- id: lee2025densesfm
  title: 'Dense-SfM: Structure from Motion with Dense Consistent Matching'
  authors: JongMin Lee, Sungjoo Yoo
  year: '2025'
  abstract: 'We present Dense-SfM, a novel Structure from Motion (SfM) framework designed
    for dense and accurate 3D reconstruction from multi-view images. Sparse keypoint
    matching, which traditional SfM methods often rely on, limits both accuracy and
    point density, especially in texture-less areas. Dense-SfM addresses this limitation
    by integrating dense matching with a Gaussian Splatting (GS) based track extension
    which gives more consistent, longer feature tracks. To further improve reconstruction
    accuracy, Dense-SfM is equipped with a multi-view kernelized matching module leveraging
    transformer and Gaussian Process architectures, for robust track refinement across
    multi-views. Evaluations on the ETH3D and Texture-Poor SfM datasets show that
    Dense-SfM offers significant improvements in accuracy and density over state-of-the-art
    methods.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.14277.pdf
  code: null
  video: null
  tags:
  - Point Cloud
  - Poses
  thumbnail: assets/thumbnails/lee2025densesfm.jpg
  publication_date: '2025-01-24T06:45:12+00:00'
  date_source: arxiv
- id: li2025micromacro
  title: Micro-macro Wavelet-based Gaussian Splatting for 3D Reconstruction from Unconstrained
    Images
  authors: Yihui Li, Chengxin Lv, Hongyu Yang, Di Huang
  year: '2025'
  abstract: '3D reconstruction from unconstrained image collections presents substantial
    challenges due to varying appearances and transient occlusions. In this paper,
    we introduce Micro-macro Wavelet-based Gaussian Splatting (MW-GS), a novel approach
    designed to enhance 3D reconstruction by disentangling scene representations into
    global, refined, and intrinsic components. The proposed method features two key
    innovations: Micro-macro Projection, which allows Gaussian points to capture details
    from feature maps across multiple scales with enhanced diversity; and Wavelet-based
    Sampling, which leverages frequency domain information to refine feature representations
    and significantly improve the modeling of scene appearances. Additionally, we
    incorporate a Hierarchical Residual Fusion Network to seamlessly integrate these
    features. Extensive experiments demonstrate that MW-GS delivers state-of-the-art
    rendering performance, surpassing existing methods.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.14231.pdf
  code: null
  video: null
  tags:
  - In the Wild
  thumbnail: assets/thumbnails/li2025micromacro.jpg
  publication_date: '2025-01-24T04:37:57+00:00'
  date_source: arxiv
- id: yu2025hammer
  title: 'HAMMER: Heterogeneous, Multi-Robot Semantic Gaussian Splatting'
  authors: Javier Yu, Timothy Chen, Mac Schwager
  year: '2025'
  abstract: '3D Gaussian Splatting offers expressive scene reconstruction, modeling
    a broad range of visual, geometric, and semantic information. However, efficient
    real-time map reconstruction with data streamed from multiple robots and devices
    remains a challenge. To that end, we propose HAMMER, a server-based collaborative
    Gaussian Splatting method that leverages widely available ROS communication infrastructure
    to generate 3D, metric-semantic maps from asynchronous robot data-streams with
    no prior knowledge of initial robot positions and varying on-device pose estimators.
    HAMMER consists of (i) a frame alignment module that transforms local SLAM poses
    and image data into a global frame and requires no prior relative pose knowledge,
    and (ii) an online module for training semantic 3DGS maps from streaming data.
    HAMMER handles mixed perception modes, adjusts automatically for variations in
    image pre-processing among different devices, and distills CLIP semantic codes
    into the 3D scene for open-vocabulary language queries. In our real-world experiments,
    HAMMER creates higher-fidelity maps (2x) compared to competing baselines and is
    useful for downstream tasks, such as semantic goal-conditioned navigation (e.g.,
    ``go to the couch"). Accompanying content available at hammer-project.github.io.

    '
  project_page: https://hammer-project.github.io/
  paper: https://arxiv.org/pdf/2501.14147.pdf
  code: null
  video: null
  tags:
  - Project
  - Robotics
  - SLAM
  thumbnail: assets/thumbnails/yu2025hammer.jpg
  publication_date: '2025-01-24T00:21:10+00:00'
  date_source: arxiv
- id: yang2025fast3r
  title: 'Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass'
  authors: Jianing Yang, Alexander Sax, Kevin J. Liang, Mikael Henaff, Hao Tang, Ang
    Cao, Joyce Chai, Franziska Meier, Matt Feiszli
  year: '2025'
  abstract: 'Multi-view 3D reconstruction remains a core challenge in computer vision,
    particularly in applications requiring accurate and scalable representations across
    diverse perspectives. Current leading methods such as DUSt3R employ a fundamentally
    pairwise approach, processing images in pairs and necessitating costly global
    alignment procedures to reconstruct from multiple views. In this work, we propose
    Fast 3D Reconstruction (Fast3R), a novel multi-view generalization to DUSt3R that
    achieves efficient and scalable 3D reconstruction by processing many views in
    parallel. Fast3R''s Transformer-based architecture forwards N images in a single
    forward pass, bypassing the need for iterative alignment. Through extensive experiments
    on camera pose estimation and 3D reconstruction, Fast3R demonstrates state-of-the-art
    performance, with significant improvements in inference speed and reduced error
    accumulation. These results establish Fast3R as a robust alternative for multi-view
    applications, offering enhanced scalability without compromising reconstruction
    accuracy.

    '
  project_page: https://fast3r-3d.github.io/
  paper: https://arxiv.org/pdf/2501.13928.pdf
  code: null
  video: null
  tags:
  - 3ster-based
  - Project
  thumbnail: assets/thumbnails/yang2025fast3r.jpg
  publication_date: '2025-01-23T18:59:55+00:00'
  date_source: arxiv
- id: sario2025gode
  title: 'GoDe: Gaussians on Demand for Progressive Level of Detail and Scalable Compression'
  authors: Francesco Di Sario, Riccardo Renzulli, Marco Grangetto, Akihiro Sugimoto,
    Enzo Tartaglione
  year: '2025'
  abstract: '3D Gaussian Splatting enhances real-time performance in novel view synthesis
    by representing scenes with mixtures of Gaussians and utilizing differentiable
    rasterization. However, it typically requires large storage capacity and high
    VRAM, demanding the design of effective pruning and compression techniques. Existing
    methods, while effective in some scenarios, struggle with scalability and fail
    to adapt models based on critical factors such as computing capabilities or bandwidth,
    requiring to re-train the model under different configurations. In this work,
    we propose a novel, model-agnostic technique that organizes Gaussians into several
    hierarchical layers, enabling progressive Level of Detail (LoD) strategy. This
    method, combined with recent approach of compression of 3DGS, allows a single
    model to instantly scale across several compression ratios, with minimal to none
    impact to quality compared to a single non-scalable model and without requiring
    re-training. We validate our approach on typical datasets and benchmarks, showcasing
    low distortion and substantial gains in terms of scalability and adaptability.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.13558.pdf
  code: null
  video: null
  tags:
  - Compression
  - LoD
  thumbnail: assets/thumbnails/sario2025gode.jpg
  publication_date: '2025-01-23T11:05:45+00:00'
  date_source: arxiv
- id: lan20253dgs2
  title: '3DGS$^2$: Near Second-order Converging 3D Gaussian Splatting'
  authors: Lei Lan, Tianjia Shao, Zixuan Lu, Yu Zhang, Chenfanfu Jiang, Yin Yang
  year: '2025'
  abstract: '3D Gaussian Splatting (3DGS) has emerged as a mainstream solution for
    novel view synthesis and 3D reconstruction. By explicitly encoding a 3D scene
    using a collection of Gaussian kernels, 3DGS achieves high-quality rendering with
    superior efficiency. As a learning-based approach, 3DGS training has been dealt
    with the standard stochastic gradient descent (SGD) method, which offers at most
    linear convergence. Consequently, training often requires tens of minutes, even
    with GPU acceleration. This paper introduces a (near) second-order convergent
    training algorithm for 3DGS, leveraging its unique properties. Our approach is
    inspired by two key observations. First, the attributes of a Gaussian kernel contribute
    independently to the image-space loss, which endorses isolated and local optimization
    algorithms. We exploit this by splitting the optimization at the level of individual
    kernel attributes, analytically constructing small-size Newton systems for each
    parameter group, and efficiently solving these systems on GPU threads. This achieves
    Newton-like convergence per training image without relying on the global Hessian.
    Second, kernels exhibit sparse and structured coupling across input images. This
    property allows us to effectively utilize spatial information to mitigate overshoot
    during stochastic training. Our method converges an order faster than standard
    GPU-based 3DGS training, requiring over $10\times$ fewer iterations while maintaining
    or surpassing the quality of the compared with the SGD-based 3DGS reconstructions.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.13975.pdf
  code: null
  video: null
  tags:
  - Optimization
  thumbnail: assets/thumbnails/lan20253dgs2.jpg
  publication_date: '2025-01-22T22:28:11+00:00'
  date_source: arxiv
- id: shi2025sketch
  title: 'Sketch and Patch: Efficient 3D Gaussian Representation for Man-Made Scenes'
  authors: Yuang Shi, Simone Gasparini, Géraldine Morin, Chenggang Yang, Wei Tsang
    Ooi
  year: '2025'
  abstract: '3D Gaussian Splatting (3DGS) has emerged as a promising representation
    for photorealistic rendering of 3D scenes. However, its high storage requirements
    pose significant challenges for practical applications. We observe that Gaussians
    exhibit distinct roles and characteristics that are analogous to traditional artistic
    techniques -- Like how artists first sketch outlines before filling in broader
    areas with color, some Gaussians capture high-frequency features like edges and
    contours; While other Gaussians represent broader, smoother regions, that are
    analogous to broader brush strokes that add volume and depth to a painting. Based
    on this observation, we propose a novel hybrid representation that categorizes
    Gaussians into (i) Sketch Gaussians, which define scene boundaries, and (ii) Patch
    Gaussians, which cover smooth regions. Sketch Gaussians are efficiently encoded
    using parametric models, leveraging their geometric coherence, while Patch Gaussians
    undergo optimized pruning, retraining, and vector quantization to maintain volumetric
    consistency and storage efficiency. Our comprehensive evaluation across diverse
    indoor and outdoor scenes demonstrates that this structure-aware approach achieves
    up to 32.62% improvement in PSNR, 19.12% in SSIM, and 45.41% in LPIPS at equivalent
    model sizes, and correspondingly, for an indoor scene, our model maintains the
    visual quality with 2.3% of the original model size.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.13045.pdf
  code: null
  video: null
  tags:
  - Densification
  thumbnail: assets/thumbnails/shi2025sketch.jpg
  publication_date: '2025-01-22T17:52:45+00:00'
  date_source: arxiv
- id: arunan2025darbsplatting
  title: 'DARB-Splatting: Generalizing Splatting with Decaying Anisotropic Radial
    Basis Functions'
  authors: Vishagar Arunan, Saeedha Nazar, Hashiru Pramuditha, Vinasirajan Viruthshaan,
    Sameera Ramasinghe, Simon Lucey, Ranga Rodrigo
  year: '2025'
  abstract: 'Splatting-based 3D reconstruction methods have gained popularity with
    the advent of 3D Gaussian Splatting, efficiently synthesizing high-quality novel
    views. These methods commonly resort to using exponential family functions, such
    as the Gaussian function, as reconstruction kernels due to their anisotropic nature,
    ease of projection, and differentiability in rasterization. However, the field
    remains restricted to variations within the exponential family, leaving generalized
    reconstruction kernels largely underexplored, partly due to the lack of easy integrability
    in 3D to 2D projections. In this light, we show that a class of decaying anisotropic
    radial basis functions (DARBFs), which are non-negative functions of the Mahalanobis
    distance, supports splatting by approximating the Gaussian function''s closed-form
    integration advantage. With this fresh perspective, we demonstrate up to 34% faster
    convergence during training and a 15% reduction in memory consumption across various
    DARB reconstruction kernels, while maintaining comparable PSNR, SSIM, and LPIPS
    results. We will make the code available.

    '
  project_page: https://randomnerds.github.io/darbs.github.io/
  paper: https://arxiv.org/pdf/2501.12369.pdf
  code: null
  video: null
  tags:
  - Project
  - Rendering
  thumbnail: assets/thumbnails/arunan2025darbsplatting.jpg
  publication_date: '2025-01-21T18:49:06+00:00'
  date_source: arxiv
- id: chen2025hac
  title: 'HAC++: Towards 100X Compression of 3D Gaussian Splatting'
  authors: Yihang Chen, Qianyi Wu, Weiyao Lin, Mehrtash Harandi, Jianfei Cai
  year: '2025'
  abstract: '3D Gaussian Splatting (3DGS) has emerged as a promising framework for
    novel view synthesis, boasting rapid rendering speed with high fidelity. However,
    the substantial Gaussians and their associated attributes necessitate effective
    compression techniques. Nevertheless, the sparse and unorganized nature of the
    point cloud of Gaussians (or anchors in our paper) presents challenges for compression.
    To achieve a compact size, we propose HAC++, which leverages the relationships
    between unorganized anchors and a structured hash grid, utilizing their mutual
    information for context modeling. Additionally, HAC++ captures intra-anchor contextual
    relationships to further enhance compression performance. To facilitate entropy
    coding, we utilize Gaussian distributions to precisely estimate the probability
    of each quantized attribute, where an adaptive quantization module is proposed
    to enable high-precision quantization of these attributes for improved fidelity
    restoration. Moreover, we incorporate an adaptive masking strategy to eliminate
    invalid Gaussians and anchors. Overall, HAC++ achieves a remarkable size reduction
    of over 100X compared to vanilla 3DGS when averaged on all datasets, while simultaneously
    improving fidelity. It also delivers more than 20X size reduction compared to
    Scaffold-GS. Our code is available at https://github.com/YihangChen-ee/HAC-plus.

    '
  project_page: https://yihangchen-ee.github.io/project_hac++/
  paper: https://arxiv.org/pdf/2501.12255.pdf
  code: https://github.com/YihangChen-ee/HAC-plus
  video: null
  tags:
  - Code
  - Compression
  - Project
  thumbnail: assets/thumbnails/chen2025hac.jpg
  publication_date: '2025-01-21T16:23:05+00:00'
  date_source: arxiv
- id: li2025cargs
  title: 'Car-GS: Addressing Reflective and Transparent Surface Challenges in 3D Car
    Reconstruction'
  authors: Congcong Li, Jin Wang, Xiaomeng Wang, Xingchen Zhou, Wei Wu, Yuzhi Zhang,
    Tongyi Cao
  year: '2025'
  abstract: '3D car modeling is crucial for applications in autonomous driving systems,
    virtual and augmented reality, and gaming. However, due to the distinctive properties
    of cars, such as highly reflective and transparent surface materials, existing
    methods often struggle to achieve accurate 3D car reconstruction.To address these
    limitations, we propose Car-GS, a novel approach designed to mitigate the effects
    of specular highlights and the coupling of RGB and geometry in 3D geometric and
    shading reconstruction (3DGS). Our method incorporates three key innovations:
    First, we introduce view-dependent Gaussian primitives to effectively model surface
    reflections. Second, we identify the limitations of using a shared opacity parameter
    for both image rendering and geometric attributes when modeling transparent objects.
    To overcome this, we assign a learnable geometry-specific opacity to each 2D Gaussian
    primitive, dedicated solely to rendering depth and normals. Third, we observe
    that reconstruction errors are most prominent when the camera view is nearly orthogonal
    to glass surfaces. To address this issue, we develop a quality-aware supervision
    module that adaptively leverages normal priors from a pre-trained large-scale
    normal model.Experimental results demonstrate that Car-GS achieves precise reconstruction
    of car surfaces and significantly outperforms prior methods. The project page
    is available at https://lcc815.github.io/Car-GS.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.11020.pdf
  code: https://lcc815.github.io/Car-GS/
  video: null
  tags:
  - Code
  - Meshing
  - Rendering
  thumbnail: assets/thumbnails/li2025cargs.jpg
  publication_date: '2025-01-19T11:49:35+00:00'
  date_source: arxiv
- id: zheng2025gstar
  title: 'GSTAR: Gaussian Surface Tracking and Reconstruction'
  authors: Chengwei Zheng, Lixin Xue, Juan Zarate, Jie Song
  year: '2025'
  abstract: '3D Gaussian Splatting techniques have enabled efficient photo-realistic
    rendering of static scenes. Recent works have extended these approaches to support
    surface reconstruction and tracking. However, tracking dynamic surfaces with 3D
    Gaussians remains challenging due to complex topology changes, such as surfaces
    appearing, disappearing, or splitting. To address these challenges, we propose
    GSTAR, a novel method that achieves photo-realistic rendering, accurate surface
    reconstruction, and reliable 3D tracking for general dynamic scenes with changing
    topology. Given multi-view captures as input, GSTAR binds Gaussians to mesh faces
    to represent dynamic objects. For surfaces with consistent topology, GSTAR maintains
    the mesh topology and tracks the meshes using Gaussians. In regions where topology
    changes, GSTAR adaptively unbinds Gaussians from the mesh, enabling accurate registration
    and the generation of new surfaces based on these optimized Gaussians. Additionally,
    we introduce a surface-based scene flow method that provides robust initialization
    for tracking between frames. Experiments demonstrate that our method effectively
    tracks and reconstructs dynamic surfaces, enabling a range of applications. Our
    project page with the code release is available at https://eth-ait.github.io/GSTAR/.

    '
  project_page: chengwei-zheng.github.io/GSTAR/
  paper: https://arxiv.org/pdf/2501.10283.pdf
  code: null
  video: https://www.youtube.com/watch?v=Fwby4PrjFeM
  tags:
  - Avatar
  - Dynamic
  - Meshing
  - Project
  - Video
  thumbnail: assets/thumbnails/zheng2025gstar.jpg
  publication_date: '2025-01-17T16:26:24+00:00'
  date_source: arxiv
- id: ma2025cityloc
  title: 'CityLoc: 6 DoF Localization of Text Descriptions in Large-Scale Scenes with
    Gaussian Representation'
  authors: Qi Ma, Runyi Yang, Bin Ren, Ender Konukoglu, Luc Van Gool, Danda Pani Paudel
  year: '2025'
  abstract: 'Localizing text descriptions in large-scale 3D scenes is inherently an
    ambiguous task. This nonetheless arises while describing general concepts, e.g.
    all traffic lights in a city.   To facilitate reasoning based on such concepts,
    text localization in the form of distribution is required. In this paper, we generate
    the distribution of the camera poses conditioned upon the textual description.
    To facilitate such generation, we propose a diffusion-based architecture that
    conditionally diffuses the noisy 6DoF camera poses to their plausible locations.
    The conditional signals are derived from the text descriptions, using the pre-trained
    text encoders. The connection between text descriptions and pose distribution
    is established through pretrained Vision-Language-Model, i.e. CLIP. Furthermore,
    we demonstrate that the candidate poses for the distribution can be further refined
    by rendering potential poses using 3D Gaussian splatting, guiding incorrectly
    posed samples towards locations that better align with the textual description,
    through visual reasoning.   We demonstrate the effectiveness of our method by
    comparing it with both standard retrieval methods and learning-based approaches.
    Our proposed method consistently outperforms these baselines across all five large-scale
    datasets. Our source code and dataset will be made publicly available.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.08982.pdf
  code: null
  video: null
  tags:
  - Language Embedding
  - Large-Scale
  thumbnail: assets/thumbnails/ma2025cityloc.jpg
  publication_date: '2025-01-15T17:59:32+00:00'
  date_source: arxiv
- id: hong2025gslivo
  title: 'GS-LIVO: Real-Time LiDAR, Inertial, and Visual Multi-sensor Fused Odometry
    with Gaussian Mapping'
  authors: Sheng Hong, Chunran Zheng, Yishu Shen, Changze Li, Fu Zhang, Tong Qin,
    Shaojie Shen
  year: '2025'
  abstract: 'In recent years, 3D Gaussian splatting (3D-GS) has emerged as a novel
    scene representation approach. However, existing vision-only 3D-GS methods often
    rely on hand-crafted heuristics for point-cloud densification and face challenges
    in handling occlusions and high GPU memory and computation consumption. LiDAR-Inertial-Visual
    (LIV) sensor configuration has demonstrated superior performance in localization
    and dense mapping by leveraging complementary sensing characteristics: rich texture
    information from cameras, precise geometric measurements from LiDAR, and high-frequency
    motion data from IMU. Inspired by this, we propose a novel real-time Gaussian-based
    simultaneous localization and mapping (SLAM) system. Our map system comprises
    a global Gaussian map and a sliding window of Gaussians, along with an IESKF-based
    odometry. The global Gaussian map consists of hash-indexed voxels organized in
    a recursive octree, effectively covering sparse spatial volumes while adapting
    to different levels of detail and scales. The Gaussian map is initialized through
    multi-sensor fusion and optimized with photometric gradients. Our system incrementally
    maintains a sliding window of Gaussians, significantly reducing GPU computation
    and memory consumption by only optimizing the map within the sliding window. Moreover,
    we implement a tightly coupled multi-sensor fusion odometry with an iterative
    error state Kalman filter (IESKF), leveraging real-time updating and rendering
    of the Gaussian map. Our system represents the first real-time Gaussian-based
    SLAM framework deployable on resource-constrained embedded systems, demonstrated
    on the NVIDIA Jetson Orin NX platform. The framework achieves real-time performance
    while maintaining robust multi-sensor fusion capabilities. All implementation
    algorithms, hardware designs, and CAD models will be publicly available.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.08672.pdf
  code: null
  video: null
  tags:
  - Large-Scale
  - Lidar
  thumbnail: assets/thumbnails/hong2025gslivo.jpg
  publication_date: '2025-01-15T09:04:56+00:00'
  date_source: arxiv
- id: wu2025vingsmono
  title: 'VINGS-Mono: Visual-Inertial Gaussian Splatting Monocular SLAM in Large Scenes'
  authors: Ke Wu, Zicheng Zhang, Muer Tie, Ziqing Ai, Zhongxue Gan, Wenchao Ding
  year: '2025'
  abstract: 'VINGS-Mono is a monocular (inertial) Gaussian Splatting (GS) SLAM framework
    designed for large scenes. The framework comprises four main components: VIO Front
    End, 2D Gaussian Map, NVS Loop Closure, and Dynamic Eraser. In the VIO Front End,
    RGB frames are processed through dense bundle adjustment and uncertainty estimation
    to extract scene geometry and poses. Based on this output, the mapping module
    incrementally constructs and maintains a 2D Gaussian map. Key components of the
    2D Gaussian Map include a Sample-based Rasterizer, Score Manager, and Pose Refinement,
    which collectively improve mapping speed and localization accuracy. This enables
    the SLAM system to handle large-scale urban environments with up to 50 million
    Gaussian ellipsoids. To ensure global consistency in large-scale scenes, we design
    a Loop Closure module, which innovatively leverages the Novel View Synthesis (NVS)
    capabilities of Gaussian Splatting for loop closure detection and correction of
    the Gaussian map. Additionally, we propose a Dynamic Eraser to address the inevitable
    presence of dynamic objects in real-world outdoor scenes. Extensive evaluations
    in indoor and outdoor environments demonstrate that our approach achieves localization
    performance on par with Visual-Inertial Odometry while surpassing recent GS/NeRF
    SLAM methods. It also significantly outperforms all existing methods in terms
    of mapping and rendering quality. Furthermore, we developed a mobile app and verified
    that our framework can generate high-quality Gaussian maps in real time using
    only a smartphone camera and a low-frequency IMU sensor. To the best of our knowledge,
    VINGS-Mono is the first monocular Gaussian SLAM method capable of operating in
    outdoor environments and supporting kilometer-scale large scenes.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.08286.pdf
  code: null
  video: null
  tags:
  - Large-Scale
  - Meshing
  - SLAM
  thumbnail: assets/thumbnails/wu2025vingsmono.jpg
  publication_date: '2025-01-14T18:01:15+00:00'
  date_source: arxiv
- id: rogge2025objectcentric
  title: 'Object-Centric 2D Gaussian Splatting: Background Removal and Occlusion-Aware
    Pruning for Compact Object Models'
  authors: Marcel Rogge, Didier Stricker
  year: '2025'
  abstract: 'Current Gaussian Splatting approaches are effective for reconstructing
    entire scenes but lack the option to target specific objects, making them computationally
    expensive and unsuitable for object-specific applications. We propose a novel
    approach that leverages object masks to enable targeted reconstruction, resulting
    in object-centric models. Additionally, we introduce an occlusion-aware pruning
    strategy to minimize the number of Gaussians without compromising quality. Our
    method reconstructs compact object models, yielding object-centric Gaussian and
    mesh representations that are up to 96\% smaller and up to 71\% faster to train
    compared to the baseline while retaining competitive quality. These representations
    are immediately usable for downstream applications such as appearance editing
    and physics simulation without additional processing.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.08174.pdf
  code: null
  video: null
  tags:
  - Compression
  - Densification
  - Editing
  thumbnail: assets/thumbnails/rogge2025objectcentric.jpg
  publication_date: '2025-01-14T14:56:31+00:00'
  date_source: arxiv
- id: liu2025uncommon
  title: UnCommon Objects in 3D
  authors: Xingchen Liu, Piyush Tayal, Jianyuan Wang, Jesus Zarzar, Tom Monnier, Konstantinos
    Tertikas, Jiali Duan, Antoine Toisoul, Jason Y. Zhang, Natalia Neverova, Andrea
    Vedaldi, Roman Shapovalov, David Novotny
  year: '2025'
  abstract: 'We introduce Uncommon Objects in 3D (uCO3D), a new object-centric dataset
    for 3D deep learning and 3D generative AI. uCO3D is the largest publicly-available
    collection of high-resolution videos of objects with 3D annotations that ensures
    full-360$^{\circ}$ coverage. uCO3D is significantly more diverse than MVImgNet
    and CO3Dv2, covering more than 1,000 object categories. It is also of higher quality,
    due to extensive quality checks of both the collected videos and the 3D annotations.
    Similar to analogous datasets, uCO3D contains annotations for 3D camera poses,
    depth maps and sparse point clouds. In addition, each object is equipped with
    a caption and a 3D Gaussian Splat reconstruction. We train several large 3D models
    on MVImgNet, CO3Dv2, and uCO3D and obtain superior results using the latter, showing
    that uCO3D is better for learning applications.

    '
  project_page: https://uco3d.github.io/
  paper: https://arxiv.org/pdf/2501.07574.pdf
  code: https://github.com/facebookresearch/uco3d
  video: null
  tags:
  - Code
  - Project
  thumbnail: assets/thumbnails/liu2025uncommon.jpg
  publication_date: '2025-01-13T18:59:20+00:00'
  date_source: arxiv
- id: stuart20253dgstopc
  title: '3DGS-to-PC: Convert a 3D Gaussian Splatting Scene into a Dense Point Cloud
    or Mesh'
  authors: Lewis A G Stuart, Michael P Pound
  year: '2025'
  abstract: '3D Gaussian Splatting (3DGS) excels at producing highly detailed 3D reconstructions,
    but these scenes often require specialised renderers for effective visualisation.
    In contrast, point clouds are a widely used 3D representation and are compatible
    with most popular 3D processing software, yet converting 3DGS scenes into point
    clouds is a complex challenge. In this work we introduce 3DGS-to-PC, a flexible
    and highly customisable framework that is capable of transforming 3DGS scenes
    into dense, high-accuracy point clouds. We sample points probabilistically from
    each Gaussian as a 3D density function. We additionally threshold new points using
    the Mahalanobis distance to the Gaussian centre, preventing extreme outliers.
    The result is a point cloud that closely represents the shape encoded into the
    3D Gaussian scene. Individual Gaussians use spherical harmonics to adapt colours
    depending on view, and each point may contribute only subtle colour hints to the
    resulting rendered scene. To avoid spurious or incorrect colours that do not fit
    with the final point cloud, we recalculate Gaussian colours via a customised image
    rendering approach, assigning each Gaussian the colour of the pixel to which it
    contributes most across all views. 3DGS-to-PC also supports mesh generation through
    Poisson Surface Reconstruction, applied to points sampled from predicted surface
    Gaussians. This allows coloured meshes to be generated from 3DGS scenes without
    the need for re-training. This package is highly customisable and capability of
    simple integration into existing 3DGS pipelines. 3DGS-to-PC provides a powerful
    tool for converting 3DGS data into point cloud and surface-based formats.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.07478.pdf
  code: https://github.com/Lewis-Stuart-11/3DGS-to-PC
  video: null
  tags:
  - Code
  - Point Cloud
  thumbnail: assets/thumbnails/stuart20253dgstopc.jpg
  publication_date: '2025-01-13T16:52:28+00:00'
  date_source: arxiv
- id: zhang2025evaluating
  title: 'Evaluating Human Perception of Novel View Synthesis: Subjective Quality
    Assessment of Gaussian Splatting and NeRF in Dynamic Scenes'
  authors: Yuhang Zhang, Joshua Maraval, Zhengyu Zhang, Nicolas Ramin, Shishun Tian,
    Lu Zhang
  year: '2025'
  abstract: 'Gaussian Splatting (GS) and Neural Radiance Fields (NeRF) are two groundbreaking
    technologies that have revolutionized the field of Novel View Synthesis (NVS),
    enabling immersive photorealistic rendering and user experiences by synthesizing
    multiple viewpoints from a set of images of sparse views. The potential applications
    of NVS, such as high-quality virtual and augmented reality, detailed 3D modeling,
    and realistic medical organ imaging, underscore the importance of quality assessment
    of NVS methods from the perspective of human perception. Although some previous
    studies have explored subjective quality assessments for NVS technology, they
    still face several challenges, especially in NVS methods selection, scenario coverage,
    and evaluation methodology. To address these challenges, we conducted two subjective
    experiments for the quality assessment of NVS technologies containing both GS-based
    and NeRF-based methods, focusing on dynamic and real-world scenes. This study
    covers 360{\deg}, front-facing, and single-viewpoint videos while providing a
    richer and greater number of real scenes. Meanwhile, it''s the first time to explore
    the impact of NVS methods in dynamic scenes with moving objects. The two types
    of subjective experiments help to fully comprehend the influences of different
    viewing paths from a human perception perspective and pave the way for future
    development of full-reference and no-reference quality metrics. In addition, we
    established a comprehensive benchmark of various state-of-the-art objective metrics
    on the proposed database, highlighting that existing methods still struggle to
    accurately capture subjective quality. The results give us some insights into
    the limitations of existing NVS methods and may promote the development of new
    NVS methods.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.08072.pdf
  code: null
  video: null
  tags:
  - Dynamic
  thumbnail: assets/thumbnails/zhang2025evaluating.jpg
  publication_date: '2025-01-13T10:01:27+00:00'
  date_source: arxiv
- id: peng2025rmavatar
  title: 'RMAvatar: Photorealistic Human Avatar Reconstruction from Monocular Video
    Based on Rectified Mesh-embedded Gaussians'
  authors: Sen Peng, Weixing Xie, Zilong Wang, Xiaohu Guo, Zhonggui Chen, Baorong
    Yang, Xiao Dong
  year: '2025'
  abstract: 'We introduce RMAvatar, a novel human avatar representation with Gaussian
    splatting embedded on mesh to learn clothed avatar from a monocular video. We
    utilize the explicit mesh geometry to represent motion and shape of a virtual
    human and implicit appearance rendering with Gaussian Splatting. Our method consists
    of two main modules: Gaussian initialization module and Gaussian rectification
    module. We embed Gaussians into triangular faces and control their motion through
    the mesh, which ensures low-frequency motion and surface deformation of the avatar.
    Due to the limitations of LBS formula, the human skeleton is hard to control complex
    non-rigid transformations. We then design a pose-related Gaussian rectification
    module to learn fine-detailed non-rigid deformations, further improving the realism
    and expressiveness of the avatar. We conduct extensive experiments on public datasets,
    RMAvatar shows state-of-the-art performance on both rendering quality and quantitative
    evaluations. Please see our project page at https://rm-avatar.github.io.

    '
  project_page: https://rm-avatar.github.io/
  paper: https://arxiv.org/pdf/2501.07104.pdf
  code: https://github.com/RMAvatar/RMAvatar
  video: null
  tags:
  - Avatar
  - Code
  - Dynamic
  - Meshing
  - Monocular
  - Project
  thumbnail: assets/thumbnails/peng2025rmavatar.jpg
  publication_date: '2025-01-13T07:32:44+00:00'
  date_source: arxiv
- id: zielonka2025synthetic
  title: Synthetic Prior for Few-Shot Drivable Head Avatar Inversion
  authors: Wojciech Zielonka, Stephan J. Garbin, Alexandros Lattas, George Kopanas,
    Paulo Gotardo, Thabo Beeler, Justus Thies, Timo Bolkart
  year: '2025'
  abstract: 'We present SynShot, a novel method for the few-shot inversion of a drivable
    head avatar based on a synthetic prior. We tackle two major challenges. First,
    training a controllable 3D generative network requires a large number of diverse
    sequences, for which pairs of images and high-quality tracked meshes are not always
    available. Second, state-of-the-art monocular avatar models struggle to generalize
    to new views and expressions, lacking a strong prior and often overfitting to
    a specific viewpoint distribution. Inspired by machine learning models trained
    solely on synthetic data, we propose a method that learns a prior model from a
    large dataset of synthetic heads with diverse identities, expressions, and viewpoints.
    With few input images, SynShot fine-tunes the pretrained synthetic prior to bridge
    the domain gap, modeling a photorealistic head avatar that generalizes to novel
    expressions and viewpoints. We model the head avatar using 3D Gaussian splatting
    and a convolutional encoder-decoder that outputs Gaussian parameters in UV texture
    space. To account for the different modeling complexities over parts of the head
    (e.g., skin vs hair), we embed the prior with explicit control for upsampling
    the number of per-part primitives. Compared to state-of-the-art monocular methods
    that require thousands of real training images, SynShot significantly improves
    novel view and expression synthesis.

    '
  project_page: https://zielon.github.io/synshot/
  paper: https://arxiv.org/pdf/2501.06903.pdf
  code: null
  video: https://www.youtube.com/watch?v=4KQQatkaSgc
  tags:
  - Avatar
  - Dynamic
  - Project
  - Sparse
  - Video
  thumbnail: assets/thumbnails/zielonka2025synthetic.jpg
  publication_date: '2025-01-12T19:01:05+00:00'
  date_source: arxiv
- id: chen2025generalized
  title: Generalized and Efficient 2D Gaussian Splatting for Arbitrary-scale Super-Resolution
  authors: Du Chen, Liyi Chen, Zhengqiang Zhang, Lei Zhang
  year: '2025'
  abstract: 'Equipped with the continuous representation capability of Multi-Layer
    Perceptron (MLP), Implicit Neural Representation (INR) has been successfully employed
    for Arbitrary-scale Super-Resolution (ASR). However, the limited receptive field
    of the linear layers in MLP restricts the representation capability of INR, while
    it is computationally expensive to query the MLP numerous times to render each
    pixel. Recently, Gaussian Splatting (GS) has shown its advantages over INR in
    both visual quality and rendering speed in 3D tasks, which motivates us to explore
    whether GS can be employed for the ASR task. However, directly applying GS to
    ASR is exceptionally challenging because the original GS is an optimization-based
    method through overfitting each single scene, while in ASR we aim to learn a single
    model that can generalize to different images and scaling factors. We overcome
    these challenges by developing two novel techniques. Firstly, to generalize GS
    for ASR, we elaborately design an architecture to predict the corresponding image-conditioned
    Gaussians of the input low-resolution image in a feed-forward manner. Secondly,
    we implement an efficient differentiable 2D GPU/CUDA-based scale-aware rasterization
    to render super-resolved images by sampling discrete RGB values from the predicted
    contiguous Gaussians. Via end-to-end training, our optimized network, namely GSASR,
    can perform ASR for any image and unseen scaling factors. Extensive experiments
    validate the effectiveness of our proposed method. The project page can be found
    at \url{https://mt-cly.github.io/GSASR.github.io/}.

    '
  project_page: https://mt-cly.github.io/GSASR.github.io/
  paper: https://arxiv.org/pdf/2501.06838.pdf
  code: null
  video: null
  tags:
  - Project
  - Super Resolution
  thumbnail: assets/thumbnails/chen2025generalized.jpg
  publication_date: '2025-01-12T15:14:58+00:00'
  date_source: arxiv
- id: wang2025f3dgaus
  title: 'F3D-Gaus: Feed-forward 3D-aware Generation on ImageNet with Cycle-Consistent
    Gaussian Splatting'
  authors: Yuxin Wang, Qianyi Wu, Dan Xu
  year: '2025'
  abstract: 'This paper tackles the problem of generalizable 3D-aware generation from
    monocular datasets, e.g., ImageNet. The key challenge of this task is learning
    a robust 3D-aware representation without multi-view or dynamic data, while ensuring
    consistent texture and geometry across different viewpoints. Although some baseline
    methods are capable of 3D-aware generation, the quality of the generated images
    still lags behind state-of-the-art 2D generation approaches, which excel in producing
    high-quality, detailed images. To address this severe limitation, we propose a
    novel feed-forward pipeline based on pixel-aligned Gaussian Splatting, coined
    as F3D-Gaus, which can produce more realistic and reliable 3D renderings from
    monocular inputs. In addition, we introduce a self-supervised cycle-consistent
    constraint to enforce cross-view consistency in the learned 3D representation.
    This training strategy naturally allows aggregation of multiple aligned Gaussian
    primitives and significantly alleviates the interpolation limitations inherent
    in single-view pixel-aligned Gaussian Splatting. Furthermore, we incorporate video
    model priors to perform geometry-aware refinement, enhancing the generation of
    fine details in wide-viewpoint scenarios and improving the model''s capability
    to capture intricate 3D textures. Extensive experiments demonstrate that our approach
    not only achieves high-quality, multi-view consistent 3D-aware generation from
    monocular datasets, but also significantly improves training and inference efficiency.

    '
  project_page: https://arxiv.org/abs/2501.06714
  paper: https://arxiv.org/pdf/2501.06714.pdf
  code: https://github.com/W-Ted/F3D-Gaus
  video: null
  tags:
  - Code
  - Feed-Forward
  - Monocular
  - Project
  thumbnail: assets/thumbnails/wang2025f3dgaus.jpg
  publication_date: '2025-01-12T04:44:44+00:00'
  date_source: arxiv
- id: asim2025met3r
  title: 'MEt3R: Measuring Multi-View Consistency in Generated Images'
  authors: Mohammad Asim, Christopher Wewer, Thomas Wimmer, Bernt Schiele, Jan Eric
    Lenssen
  year: '2025'
  abstract: 'We introduce MEt3R, a metric for multi-view consistency in generated
    images. Large-scale generative models for multi-view image generation are rapidly
    advancing the field of 3D inference from sparse observations. However, due to
    the nature of generative modeling, traditional reconstruction metrics are not
    suitable to measure the quality of generated outputs and metrics that are independent
    of the sampling procedure are desperately needed. In this work, we specifically
    address the aspect of consistency between generated multi-view images, which can
    be evaluated independently of the specific scene. Our approach uses DUSt3R to
    obtain dense 3D reconstructions from image pairs in a feed-forward manner, which
    are used to warp image contents from one view into the other. Then, feature maps
    of these images are compared to obtain a similarity score that is invariant to
    view-dependent effects. Using MEt3R, we evaluate the consistency of a large set
    of previous methods for novel view and video generation, including our open, multi-view
    latent diffusion model.

    '
  project_page: https://geometric-rl.mpi-inf.mpg.de/met3r/
  paper: https://arxiv.org/pdf/2501.06336.pdf
  code: https://github.com/mohammadasim98/MEt3R
  video: https://geometric-rl.mpi-inf.mpg.de/met3r/static/videos/teaser.mp4
  tags:
  - 3ster-based
  - Code
  - Diffusion
  - Project
  - Video
  thumbnail: assets/thumbnails/asim2025met3r.jpg
  publication_date: '2025-01-10T20:43:33+00:00'
  date_source: arxiv
- id: shin2025localityaware
  title: Locality-aware Gaussian Compression for Fast and High-quality Rendering
  authors: Seungjoo Shin, Jaesik Park, Sunghyun Cho
  year: '2025'
  abstract: 'We present LocoGS, a locality-aware 3D Gaussian Splatting (3DGS) framework
    that exploits the spatial coherence of 3D Gaussians for compact modeling of volumetric
    scenes. To this end, we first analyze the local coherence of 3D Gaussian attributes,
    and propose a novel locality-aware 3D Gaussian representation that effectively
    encodes locally-coherent Gaussian attributes using a neural field representation
    with a minimal storage requirement. On top of the novel representation, LocoGS
    is carefully designed with additional components such as dense initialization,
    an adaptive spherical harmonics bandwidth scheme and different encoding schemes
    for different Gaussian attributes to maximize compression performance. Experimental
    results demonstrate that our approach outperforms the rendering quality of existing
    compact Gaussian representations for representative real-world 3D datasets while
    achieving from 54.6$\times$ to 96.6$\times$ compressed storage size and from 2.1$\times$
    to 2.4$\times$ rendering speed than 3DGS. Even our approach also demonstrates
    an averaged 2.4$\times$ higher rendering speed than the state-of-the-art compression
    method with comparable compression performance.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.05757.pdf
  code: null
  video: null
  tags:
  - Compression
  thumbnail: assets/thumbnails/shin2025localityaware.jpg
  publication_date: '2025-01-10T07:19:41+00:00'
  date_source: arxiv
- id: yan2025consistent
  title: Consistent Flow Distillation for Text-to-3D Generation
  authors: Runjie Yan, Yinbo Chen, Xiaolong Wang
  year: '2025'
  abstract: 'Score Distillation Sampling (SDS) has made significant strides in distilling
    image-generative models for 3D generation. However, its maximum-likelihood-seeking
    behavior often leads to degraded visual quality and diversity, limiting its effectiveness
    in 3D applications. In this work, we propose Consistent Flow Distillation (CFD),
    which addresses these limitations. We begin by leveraging the gradient of the
    diffusion ODE or SDE sampling process to guide the 3D generation. From the gradient-based
    sampling perspective, we find that the consistency of 2D image flows across different
    viewpoints is important for high-quality 3D generation. To achieve this, we introduce
    multi-view consistent Gaussian noise on the 3D object, which can be rendered from
    various viewpoints to compute the flow gradient. Our experiments demonstrate that
    CFD, through consistent flows, significantly outperforms previous methods in text-to-3D
    generation.

    '
  project_page: https://runjie-yan.github.io/cfd/
  paper: https://arxiv.org/pdf/2501.05445.pdf
  code: https://github.com/runjie-yan/ConsistentFlowDistillation
  video: null
  tags:
  - Code
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/yan2025consistent.jpg
  publication_date: '2025-01-09T18:56:05+00:00'
  date_source: arxiv
- id: meng2025zero1tog
  title: 'Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation'
  authors: Xuyi Meng, Chen Wang, Jiahui Lei, Kostas Daniilidis, Jiatao Gu, Lingjie
    Liu
  year: '2025'
  abstract: 'Recent advances in 2D image generation have achieved remarkable quality,largely
    driven by the capacity of diffusion models and the availability of large-scale
    datasets. However, direct 3D generation is still constrained by the scarcity and
    lower fidelity of 3D datasets. In this paper, we introduce Zero-1-to-G, a novel
    approach that addresses this problem by enabling direct single-view generation
    on Gaussian splats using pretrained 2D diffusion models. Our key insight is that
    Gaussian splats, a 3D representation, can be decomposed into multi-view images
    encoding different attributes. This reframes the challenging task of direct 3D
    generation within a 2D diffusion framework, allowing us to leverage the rich priors
    of pretrained 2D diffusion models. To incorporate 3D awareness, we introduce cross-view
    and cross-attribute attention layers, which capture complex correlations and enforce
    3D consistency across generated splats. This makes Zero-1-to-G the first direct
    image-to-3D generative model to effectively utilize pretrained 2D diffusion priors,
    enabling efficient training and improved generalization to unseen objects. Extensive
    experiments on both synthetic and in-the-wild datasets demonstrate superior performance
    in 3D object generation, offering a new approach to high-quality 3D generation.

    '
  project_page: https://mengxuyigit.github.io/projects/zero-1-to-G/
  paper: https://arxiv.org/pdf/2501.05427.pdf
  code: null
  video: null
  tags:
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/meng2025zero1tog.jpg
  publication_date: '2025-01-09T18:37:35+00:00'
  date_source: arxiv
- id: gerogiannis2025arc2avatar
  title: 'Arc2Avatar: Generating Expressive 3D Avatars from a Single Image via ID
    Guidance'
  authors: Dimitrios Gerogiannis, Foivos Paraperas Papantoniou, Rolandos Alexandros
    Potamias, Alexandros Lattas, Stefanos Zafeiriou
  year: '2025'
  abstract: 'Inspired by the effectiveness of 3D Gaussian Splatting (3DGS) in reconstructing
    detailed 3D scenes within multi-view setups and the emergence of large 2D human
    foundation models, we introduce Arc2Avatar, the first SDS-based method utilizing
    a human face foundation model as guidance with just a single image as input. To
    achieve that, we extend such a model for diverse-view human head generation by
    fine-tuning on synthetic data and modifying its conditioning. Our avatars maintain
    a dense correspondence with a human face mesh template, allowing blendshape-based
    expression generation. This is achieved through a modified 3DGS approach, connectivity
    regularizers, and a strategic initialization tailored for our task. Additionally,
    we propose an optional efficient SDS-based correction step to refine the blendshape
    expressions, enhancing realism and diversity. Experiments demonstrate that Arc2Avatar
    achieves state-of-the-art realism and identity preservation, effectively addressing
    color issues by allowing the use of very low guidance, enabled by our strong identity
    prior and initialization strategy, without compromising detail.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.05379.pdf
  code: null
  video: null
  tags:
  - Avatar
  - Diffusion
  thumbnail: assets/thumbnails/gerogiannis2025arc2avatar.jpg
  publication_date: '2025-01-09T17:04:33+00:00'
  date_source: arxiv
- id: tianci2025scaffoldslam
  title: 'Scaffold-SLAM: Structured 3D Gaussians for Simultaneous Localization and
    Photorealistic Mapping'
  authors: Wen Tianci, Liu Zhiang, Lu Biao, Fang Yongchun
  year: '2025'
  abstract: '3D Gaussian Splatting (3DGS) has recently revolutionized novel view synthesis
    in the Simultaneous Localization and Mapping (SLAM). However, existing SLAM methods
    utilizing 3DGS have failed to provide high-quality novel view rendering for monocular,
    stereo, and RGB-D cameras simultaneously. Notably, some methods perform well for
    RGB-D cameras but suffer significant degradation in rendering quality for monocular
    cameras. In this paper, we present Scaffold-SLAM, which delivers simultaneous
    localization and high-quality photorealistic mapping across monocular, stereo,
    and RGB-D cameras. We introduce two key innovations to achieve this state-of-the-art
    visual quality. First, we propose Appearance-from-Motion embedding, enabling 3D
    Gaussians to better model image appearance variations across different camera
    poses. Second, we introduce a frequency regularization pyramid to guide the distribution
    of Gaussians, allowing the model to effectively capture finer details in the scene.
    Extensive experiments on monocular, stereo, and RGB-D datasets demonstrate that
    Scaffold-SLAM significantly outperforms state-of-the-art methods in photorealistic
    mapping quality, e.g., PSNR is 16.76% higher in the TUM RGB-D datasets for monocular
    cameras.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.05242.pdf
  code: null
  video: null
  tags:
  - SLAM
  thumbnail: assets/thumbnails/tianci2025scaffoldslam.jpg
  publication_date: '2025-01-09T13:50:26+00:00'
  date_source: arxiv
- id: bond2025gaussianvideo
  title: 'GaussianVideo: Efficient Video Representation via Hierarchical Gaussian
    Splatting'
  authors: Andrew Bond, Jui-Hsien Wang, Long Mai, Erkut Erdem, Aykut Erdem
  year: '2025'
  abstract: 'Efficient neural representations for dynamic video scenes are critical
    for applications ranging from video compression to interactive simulations. Yet,
    existing methods often face challenges related to high memory usage, lengthy training
    times, and temporal consistency. To address these issues, we introduce a novel
    neural video representation that combines 3D Gaussian splatting with continuous
    camera motion modeling. By leveraging Neural ODEs, our approach learns smooth
    camera trajectories while maintaining an explicit 3D scene representation through
    Gaussians. Additionally, we introduce a spatiotemporal hierarchical learning strategy,
    progressively refining spatial and temporal features to enhance reconstruction
    quality and accelerate convergence. This memory-efficient approach achieves high-quality
    rendering at impressive speeds. Experimental results show that our hierarchical
    learning, combined with robust camera motion modeling, captures complex dynamic
    scenes with strong temporal consistency, achieving state-of-the-art performance
    across diverse video datasets in both high- and low-motion scenarios.

    '
  project_page: https://cyberiada.github.io/GaussianVideo/
  paper: https://arxiv.org/pdf/2501.04782.pdf
  code: null
  video: null
  tags:
  - Gaussian Video
  - Project
  - Video
  thumbnail: assets/thumbnails/bond2025gaussianvideo.jpg
  publication_date: '2025-01-08T19:01:12+00:00'
  date_source: arxiv
- id: huang2025fatesgs
  title: 'FatesGS: Fast and Accurate Sparse-View Surface Reconstruction using Gaussian
    Splatting with Depth-Feature Consistency'
  authors: Han Huang, Yulun Wu, Chao Deng, Ge Gao, Ming Gu, Yu-Shen Liu
  year: '2025'
  abstract: 'Recently, Gaussian Splatting has sparked a new trend in the field of
    computer vision. Apart from novel view synthesis, it has also been extended to
    the area of multi-view reconstruction. The latest methods facilitate complete,
    detailed surface reconstruction while ensuring fast training speed. However, these
    methods still require dense input views, and their output quality significantly
    degrades with sparse views. We observed that the Gaussian primitives tend to overfit
    the few training views, leading to noisy floaters and incomplete reconstruction
    surfaces. In this paper, we present an innovative sparse-view reconstruction framework
    that leverages intra-view depth and multi-view feature consistency to achieve
    remarkably accurate surface reconstruction. Specifically, we utilize monocular
    depth ranking information to supervise the consistency of depth distribution within
    patches and employ a smoothness loss to enhance the continuity of the distribution.
    To achieve finer surface reconstruction, we optimize the absolute position of
    depth through multi-view projection features. Extensive experiments on DTU and
    BlendedMVS demonstrate that our method outperforms state-of-the-art methods with
    a speedup of 60x to 200x, achieving swift and fine-grained mesh reconstruction
    without the need for costly pre-training.

    '
  project_page: https://alvin528.github.io/FatesGS/
  paper: https://arxiv.org/pdf/2501.04628.pdf
  code: null
  video: null
  tags:
  - Meshing
  - Project
  - Sparse
  thumbnail: assets/thumbnails/huang2025fatesgs.jpg
  publication_date: '2025-01-08T17:19:35+00:00'
  date_source: arxiv
- id: kwak2025modecgs
  title: 'MoDec-GS: Global-to-Local Motion Decomposition and Temporal Interval Adjustment
    for Compact Dynamic 3D Gaussian Splatting'
  authors: Sangwoon Kwak, Joonsoo Kim, Jun Young Jeong, Won-Sik Cheong, Jihyong Oh,
    Munchurl Kim
  year: '2025'
  abstract: '3D Gaussian Splatting (3DGS) has made significant strides in scene representation
    and neural rendering, with intense efforts focused on adapting it for dynamic
    scenes. Despite delivering remarkable rendering quality and speed, existing methods
    struggle with storage demands and representing complex real-world motions. To
    tackle these issues, we propose MoDecGS, a memory-efficient Gaussian splatting
    framework designed for reconstructing novel views in challenging scenarios with
    complex motions. We introduce GlobaltoLocal Motion Decomposition (GLMD) to effectively
    capture dynamic motions in a coarsetofine manner. This approach leverages Global
    Canonical Scaffolds (Global CS) and Local Canonical Scaffolds (Local CS), extending
    static Scaffold representation to dynamic video reconstruction. For Global CS,
    we propose Global Anchor Deformation (GAD) to efficiently represent global dynamics
    along complex motions, by directly deforming the implicit Scaffold attributes
    which are anchor position, offset, and local context features. Next, we finely
    adjust local motions via the Local Gaussian Deformation (LGD) of Local CS explicitly.
    Additionally, we introduce Temporal Interval Adjustment (TIA) to automatically
    control the temporal coverage of each Local CS during training, allowing MoDecGS
    to find optimal interval assignments based on the specified number of temporal
    segments. Extensive evaluations demonstrate that MoDecGS achieves an average 70%
    reduction in model size over stateoftheart methods for dynamic 3D Gaussians from
    realworld dynamic videos while maintaining or even improving rendering quality.

    '
  project_page: 'MoDec-GS: Global-to-Local Motion Decomposition and Temporal Interval
    Adjustment for Compact Dynamic 3D Gaussian Splatting'
  paper: https://arxiv.org/pdf/2501.03714.pdf
  code: null
  video: https://youtu.be/5L6gzc5-cw8?si=L6v6XLZFQrYK50iV
  tags:
  - Compression
  - Dynamic
  - Project
  - Video
  thumbnail: assets/thumbnails/kwak2025modecgs.jpg
  publication_date: '2025-01-07T11:43:13+00:00'
  date_source: arxiv
- id: yu2025dehazegs
  title: 'DehazeGS: Seeing Through Fog with 3D Gaussian Splatting'
  authors: Jinze Yu, Yiqun Wang, Zhengda Lu, Jianwei Guo, Yong Li, Hongxing Qin, Xiaopeng
    Zhang
  year: '2025'
  abstract: 'Current novel view synthesis tasks primarily rely on high-quality and
    clear images. However, in foggy scenes, scattering and attenuation can significantly
    degrade the reconstruction and rendering quality. Although NeRF-based dehazing
    reconstruction algorithms have been developed, their use of deep fully connected
    neural networks and per-ray sampling strategies leads to high computational costs.
    Moreover, NeRF''s implicit representation struggles to recover fine details from
    hazy scenes. In contrast, recent advancements in 3D Gaussian Splatting achieve
    high-quality 3D scene reconstruction by explicitly modeling point clouds into
    3D Gaussians. In this paper, we propose leveraging the explicit Gaussian representation
    to explain the foggy image formation process through a physically accurate forward
    rendering process. We introduce DehazeGS, a method capable of decomposing and
    rendering a fog-free background from participating media using only muti-view
    foggy images as input. We model the transmission within each Gaussian distribution
    to simulate the formation of fog. During this process, we jointly learn the atmospheric
    light and scattering coefficient while optimizing the Gaussian representation
    of the hazy scene. In the inference stage, we eliminate the effects of scattering
    and attenuation on the Gaussians and directly project them onto a 2D plane to
    obtain a clear view. Experiments on both synthetic and real-world foggy datasets
    demonstrate that DehazeGS achieves state-of-the-art performance in terms of both
    rendering quality and computational efficiency.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.03659.pdf
  code: null
  video: null
  tags:
  - In the Wild
  - Rendering
  thumbnail: assets/thumbnails/yu2025dehazegs.jpg
  publication_date: '2025-01-07T09:47:46+00:00'
  date_source: arxiv
- id: lee2025compression
  title: Compression of 3D Gaussian Splatting with Optimized Feature Planes and Standard
    Video Codecs
  authors: Soonbin Lee, Fangwen Shu, Yago Sanchez, Thomas Schierl, Cornelius Hellge
  year: '2025'
  abstract: '3D Gaussian Splatting is a recognized method for 3D scene representation,
    known for its high rendering quality and speed. However, its substantial data
    requirements present challenges for practical applications. In this paper, we
    introduce an efficient compression technique that significantly reduces storage
    overhead by using compact representation. We propose a unified architecture that
    combines point cloud data and feature planes through a progressive tri-plane structure.
    Our method utilizes 2D feature planes, enabling continuous spatial representation.
    To further optimize these representations, we incorporate entropy modeling in
    the frequency domain, specifically designed for standard video codecs. We also
    propose channel-wise bit allocation to achieve a better trade-off between bitrate
    consumption and feature plane representation. Consequently, our model effectively
    leverages spatial correlations within the feature planes to enhance rate-distortion
    performance using standard, non-differentiable video codecs. Experimental results
    demonstrate that our method outperforms existing methods in data compactness while
    maintaining high rendering quality. Our project page is available at https://fraunhoferhhi.github.io/CodecGS

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.03399.pdf
  code: null
  video: null
  tags:
  - Compression
  thumbnail: assets/thumbnails/lee2025compression.jpg
  publication_date: '2025-01-06T21:37:30+00:00'
  date_source: arxiv
- id: rajasegaran2025gaussian
  title: Gaussian Masked Autoencoders
  authors: Jathushan Rajasegaran, Xinlei Chen, Rulilong Li, Christoph Feichtenhofer,
    Jitendra Malik, Shiry Ginosar
  year: '2025'
  abstract: 'This paper explores Masked Autoencoders (MAE) with Gaussian Splatting.
    While reconstructive self-supervised learning frameworks such as MAE learns good
    semantic abstractions, it is not trained for explicit spatial awareness. Our approach,
    named Gaussian Masked Autoencoder, or GMAE, aims to learn semantic abstractions
    and spatial understanding jointly. Like MAE, it reconstructs the image end-to-end
    in the pixel space, but beyond MAE, it also introduces an intermediate, 3D Gaussian-based
    representation and renders images via splatting. We show that GMAE can enable
    various zero-shot learning capabilities of spatial understanding (e.g., figure-ground
    segmentation, image layering, edge detection, etc.) while preserving the high-level
    semantics of self-supervised representation quality from MAE. To our knowledge,
    we are the first to employ Gaussian primitives in an image representation learning
    framework beyond optimization-based single-scene reconstructions. We believe GMAE
    will inspire further research in this direction and contribute to developing next-generation
    techniques for modeling high-fidelity visual data. More details at https://brjathu.github.io/gmae

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.03229.pdf
  code: https://github.com/darshanmakwana412/gaussian-mae
  video: null
  tags:
  - Code
  - Transformer
  thumbnail: assets/thumbnails/rajasegaran2025gaussian.jpg
  publication_date: '2025-01-06T18:59:57+00:00'
  date_source: arxiv
- id: nguyen2025pointmapconditioned
  title: Pointmap-Conditioned Diffusion for Consistent Novel View Synthesis
  authors: Thang-Anh-Quan Nguyen, Nathan Piasco, Luis Roldão, Moussab Bennehar, Dzmitry
    Tsishkou, Laurent Caraffa, Jean-Philippe Tarel, Roland Brémond
  year: '2025'
  abstract: 'In this paper, we present PointmapDiffusion, a novel framework for single-image
    novel view synthesis (NVS) that utilizes pre-trained 2D diffusion models. Our
    method is the first to leverage pointmaps (i.e. rasterized 3D scene coordinates)
    as a conditioning signal, capturing geometric prior from the reference images
    to guide the diffusion process. By embedding reference attention blocks and a
    ControlNet for pointmap features, our model balances between generative capability
    and geometric consistency, enabling accurate view synthesis across varying viewpoints.
    Extensive experiments on diverse real-world datasets demonstrate that PointmapDiffusion
    achieves high-quality, multi-view consistent results with significantly fewer
    trainable parameters compared to other baselines for single-image NVS tasks.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.02913.pdf
  code: null
  video: null
  tags:
  - Diffusion
  thumbnail: assets/thumbnails/nguyen2025pointmapconditioned.jpg
  publication_date: '2025-01-06T10:48:31+00:00'
  date_source: arxiv
- id: bian2025gsdit
  title: 'GS-DiT: Advancing Video Generation with Pseudo 4D Gaussian Fields through
    Efficient Dense 3D Point Tracking'
  authors: Weikang Bian, Zhaoyang Huang, Xiaoyu Shi, Yijin Li, Fu-Yun Wang, Hongsheng
    Li
  year: '2025'
  abstract: '4D video control is essential in video generation as it enables the use
    of sophisticated lens techniques, such as multi-camera shooting and dolly zoom,
    which are currently unsupported by existing methods. Training a video Diffusion
    Transformer (DiT) directly to control 4D content requires expensive multi-view
    videos. Inspired by Monocular Dynamic novel View Synthesis (MDVS) that optimizes
    a 4D representation and renders videos according to different 4D elements, such
    as camera pose and object motion editing, we bring pseudo 4D Gaussian fields to
    video generation. Specifically, we propose a novel framework that constructs a
    pseudo 4D Gaussian field with dense 3D point tracking and renders the Gaussian
    field for all video frames. Then we finetune a pretrained DiT to generate videos
    following the guidance of the rendered video, dubbed as GS-DiT. To boost the training
    of the GS-DiT, we also propose an efficient Dense 3D Point Tracking (D3D-PT) method
    for the pseudo 4D Gaussian field construction. Our D3D-PT outperforms SpatialTracker,
    the state-of-the-art sparse 3D point tracking method, in accuracy and accelerates
    the inference speed by two orders of magnitude. During the inference stage, GS-DiT
    can generate videos with the same dynamic content while adhering to different
    camera parameters, addressing a significant limitation of current video generation
    models. GS-DiT demonstrates strong generalization capabilities and extends the
    4D controllability of Gaussian splatting to video generation beyond just camera
    poses. It supports advanced cinematic effects through the manipulation of the
    Gaussian field and camera intrinsics, making it a powerful tool for creative video
    production. Demos are available at https://wkbian.github.io/Projects/GS-DiT/.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.02690.pdf
  code: null
  video: null
  tags:
  - Year 2025
  thumbnail: assets/thumbnails/bian2025gsdit.jpg
  publication_date: '2025-01-05T23:55:33+00:00'
  date_source: arxiv
- id: cong2025videolifter
  title: 'VideoLifter: Lifting Videos to 3D with Fast Hierarchical Stereo Alignment'
  authors: Wenyan Cong, Kevin Wang, Jiahui Lei, Colton Stearns, Yuanhao Cai, Dilin
    Wang, Rakesh Ranjan, Matt Feiszli, Leonidas Guibas, Zhangyang Wang, Weiyao Wang,
    Zhiwen Fan
  year: '2025'
  abstract: 'Efficiently reconstructing accurate 3D models from monocular video is
    a key challenge in computer vision, critical for advancing applications in virtual
    reality, robotics, and scene understanding. Existing approaches typically require
    pre-computed camera parameters and frame-by-frame reconstruction pipelines, which
    are prone to error accumulation and entail significant computational overhead.
    To address these limitations, we introduce VideoLifter, a novel framework that
    leverages geometric priors from a learnable model to incrementally optimize a
    globally sparse to dense 3D representation directly from video sequences. VideoLifter
    segments the video sequence into local windows, where it matches and registers
    frames, constructs consistent fragments, and aligns them hierarchically to produce
    a unified 3D model. By tracking and propagating sparse point correspondences across
    frames and fragments, VideoLifter incrementally refines camera poses and 3D structure,
    minimizing reprojection error for improved accuracy and robustness. This approach
    significantly accelerates the reconstruction process, reducing training time by
    over 82% while surpassing current state-of-the-art methods in visual fidelity
    and computational efficiency.

    '
  project_page: https://videolifter.github.io/
  paper: https://arxiv.org/pdf/2501.01949.pdf
  code: https://github.com/VITA-Group/VideoLifter
  video: null
  tags:
  - Acceleration
  - Code
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/cong2025videolifter.jpg
  publication_date: '2025-01-03T18:52:36+00:00'
  date_source: arxiv
- id: huang2025enerverse
  title: 'EnerVerse: Envisioning Embodied Future Space for Robotics Manipulation'
  authors: Siyuan Huang, Liliang Chen, Pengfei Zhou, Shengcong Chen, Zhengkai Jiang,
    Yue Hu, Peng Gao, Hongsheng Li, Maoqing Yao, Guanghui Ren
  year: '2025'
  abstract: 'We introduce EnerVerse, a comprehensive framework for embodied future
    space generation specifically designed for robotic manipulation tasks. EnerVerse
    seamlessly integrates convolutional and bidirectional attention mechanisms for
    inner-chunk space modeling, ensuring low-level consistency and continuity. Recognizing
    the inherent redundancy in video data, we propose a sparse memory context combined
    with a chunkwise unidirectional generative paradigm to enable the generation of
    infinitely long sequences. To further augment robotic capabilities, we introduce
    the Free Anchor View (FAV) space, which provides flexible perspectives to enhance
    observation and analysis. The FAV space mitigates motion modeling ambiguity, removes
    physical constraints in confined environments, and significantly improves the
    robot''s generalization and adaptability across various tasks and settings. To
    address the prohibitive costs and labor intensity of acquiring multi-camera observations,
    we present a data engine pipeline that integrates a generative model with 4D Gaussian
    Splatting (4DGS). This pipeline leverages the generative model''s robust generalization
    capabilities and the spatial constraints provided by 4DGS, enabling an iterative
    enhancement of data quality and diversity, thus creating a data flywheel effect
    that effectively narrows the sim-to-real gap. Finally, our experiments demonstrate
    that the embodied future space generation prior substantially enhances policy
    predictive capabilities, resulting in improved overall performance, particularly
    in long-range robotic manipulation tasks.

    '
  project_page: https://sites.google.com/view/enerverse
  paper: https://arxiv.org/pdf/2501.01895.pdf
  code: null
  video: null
  tags:
  - Dynamic
  - Project
  - Robotics
  thumbnail: assets/thumbnails/huang2025enerverse.jpg
  publication_date: '2025-01-03T17:00:33+00:00'
- id: longhini2024clothsplatting
  title: 'Cloth-Splatting: 3D Cloth State Estimation from RGB Supervision'
  authors: Alberta Longhini, Marcel Büsching, Bardienus Pieter Duisterhof, Jens Lundell,
    Jeffrey Ichnowski, Mårten Björkman, Danica Kragic
  year: '2024'
  abstract: Recently, 3D Gaussian Splatting (3DGS) has revolutionized radiance field
    reconstruction, manifesting efficient and high-fidelity novel view synthesis.
    However, accurately We introduce Cloth-Splatting, a method for estimating 3D states
    of cloth from RGB images through a prediction-update framework. Cloth-Splatting
    leverages an action-conditioned dynamics model for predicting future states and
    uses 3D Gaussian Splatting to update the predicted states. Our key insight is
    that coupling a 3D mesh-based representation with Gaussian Splatting allows us
    to define a differentiable map between the cloth's state space and the image space.
    This enables the use of gradient-based optimization techniques to refine inaccurate
    state estimates using only RGB supervision. Our experiments demonstrate that Cloth-Splatting
    not only improves state estimation accuracy over current baselines but also reduces
    convergence time by ~85%.
  project_page: https://kth-rpl.github.io/cloth-splatting/
  paper: https://arxiv.org/pdf/2501.01715.pdf
  code: https://github.com/KTH-RPL/cloth-splatting
  video: null
  tags:
  - Code
  - Meshing
  - Project
  - Rendering
  thumbnail: assets/thumbnails/longhini2024clothsplatting.jpg
  publication_date: '2025-01-03T09:17:30+00:00'
  date_source: arxiv
- id: zhang2025crossviewgs
  title: 'CrossView-GS: Cross-view Gaussian Splatting For Large-scale Scene Reconstruction'
  authors: Chenhao Zhang, Yuanping Cao, Lei Zhang
  year: '2025'
  abstract: 3D Gaussian Splatting (3DGS) has emerged as a prominent method for scene
    representation and reconstruction, leveraging densely distributed Gaussian primitives
    to enable real-time rendering of high-resolution images. While existing 3DGS methods
    perform well in scenes with minor view variation, large view changes in cross-view
    scenes pose optimization challenges for these methods. To address these issues,
    we propose a novel cross-view Gaussian Splatting method for large-scale scene
    reconstruction, based on dual-branch fusion. Our method independently reconstructs
    models from aerial and ground views as two independent branches to establish the
    baselines of Gaussian distribution, providing reliable priors for cross-view reconstruction
    during both initialization and densification. Specifically, a gradient-aware regularization
    strategy is introduced to mitigate smoothing issues caused by significant view
    disparities. Additionally, a unique Gaussian supplementation strategy is utilized
    to incorporate complementary information of dual-branch into the cross-view model.
    Extensive experiments on benchmark datasets demonstrate that our method achieves
    superior performance in novel view synthesis compared to state-of-the-art methods.
  project_page: null
  paper: https://arxiv.org/pdf/2501.01695.pdf
  code: null
  video: null
  tags:
  - Large-Scale
  - Optimization
  thumbnail: assets/thumbnails/zhang2025crossviewgs.jpg
  publication_date: '2025-01-03T08:24:59+00:00'
- id: wang2025pgsag
  title: 'PG-SAG: Parallel Gaussian Splatting for Fine-Grained Large-Scale Urban Buildings
    Reconstruction via Semantic-Aware Grouping'
  authors: Tengfei Wang, Xin Wang, Yongmao Hou, Yiwei Xu, Wendi Zhang, Zongqian Zhan
  year: '2025'
  abstract: 3D Gaussian Splatting (3DGS) has emerged as a transformative method in
    the field of real-time novel synthesis. Based on 3DGS, recent advancements cope
    with large-scale scenes via spatial-based partition strategy to reduce video memory
    and optimization time costs. In this work, we introduce a parallel Gaussian splatting
    method, termed PG-SAG, which fully exploits semantic cues for both partitioning
    and Gaussian kernel optimization, enabling fine-grained building surface reconstruction
    of large-scale urban areas without downsampling the original image resolution.
    First, the Cross-modal model - Language Segment Anything is leveraged to segment
    building masks. Then, the segmented building regions is grouped into sub-regions
    according to the visibility check across registered images. The Gaussian kernels
    for these sub-regions are optimized in parallel with masked pixels. In addition,
    the normal loss is re-formulated for the detected edges of masks to alleviate
    the ambiguities in normal vectors on edges. Finally, to improve the optimization
    of 3D Gaussians, we introduce a gradient-constrained balance-load loss that accounts
    for the complexity of the corresponding scenes, effectively minimizing the thread
    waiting time in the pixel-parallel rendering stage as well as the reconstruction
    lost. Extensive experiments are tested on various urban datasets, the results
    demonstrated the superior performance of our PG-SAG on building surface reconstruction,
    compared to several state-of-the-art 3DGS-based methods.
  project_page: null
  paper: https://arxiv.org/pdf/2501.01677.pdf
  code: null
  video: null
  tags:
  - Large-Scale
  - Meshing
  - Optimization
  thumbnail: assets/thumbnails/wang2025pgsag.jpg
  publication_date: '2025-01-03T07:40:16+00:00'
- id: gao2025easysplat
  title: 'EasySplat: View-Adaptive Learning makes 3D Gaussian Splatting Easy'
  authors: Ao Gao, Luosong Guo, Tao Chen, Zhao Wang, Ying Tai, Jian Yang, Zhenyu Zhang
  year: '2025'
  abstract: 3D Gaussian Splatting (3DGS) techniques have achieved satisfactory 3D
    scene representation. Despite their impressive performance, they confront challenges
    due to the limitation of structure-from-motion (SfM) methods on acquiring accurate
    scene initialization, or the inefficiency of densification strategy. In this paper,
    we introduce a novel framework EasySplat to achieve high-quality 3DGS modeling.
    Instead of using SfM for scene initialization, we employ a novel method to release
    the power of large-scale pointmap approaches. Specifically, we propose an efficient
    grouping strategy based on view similarity, and use robust pointmap priors to
    obtain high-quality point clouds and camera poses for 3D scene initialization.
    After obtaining a reliable scene structure, we propose a novel densification approach
    that adaptively splits Gaussian primitives based on the average shape of neighboring
    Gaussian ellipsoids, utilizing KNN scheme. In this way, the proposed method tackles
    the limitation on initialization and optimization, leading to an efficient and
    accurate 3DGS modeling. Extensive experiments demonstrate that EasySplat outperforms
    the current state-of-the-art (SOTA) in handling novel view synthesis.
  project_page: null
  paper: https://arxiv.org/pdf/2501.01003.pdf
  code: null
  video: null
  tags:
  - 3ster-based
  - Acceleration
  - Densification
  - Rendering
  thumbnail: assets/thumbnails/gao2025easysplat.jpg
  publication_date: '2025-01-02T01:56:58+00:00'
- id: yang2024storm
  title: 'STORM: Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes'
  authors: Jiawei Yang, Jiahui Huang, Yuxiao Chen, Yan Wang, Boyi Li, Yurong You,
    Apoorva Sharma, Maximilian Igl, Peter Karkus, Danfei Xu, Boris Ivanovic, Yue Wang,
    Marco Pavone
  year: '2024'
  abstract: We present STORM, a spatio-temporal reconstruction model designed for
    reconstructing dynamic outdoor scenes from sparse observations. Existing dynamic
    reconstruction methods often rely on per-scene optimization, dense observations
    across space and time, and strong motion supervision, resulting in lengthy optimization
    times, limited generalization to novel views or scenes, and degenerated quality
    caused by noisy pseudo-labels for dynamics. To address these challenges, STORM
    leverages a data-driven Transformer architecture that directly infers dynamic
    3D scene representations--parameterized by 3D Gaussians and their velocities--in
    a single forward pass. Our key design is to aggregate 3D Gaussians from all frames
    using self-supervised scene flows, transforming them to the target timestep to
    enable complete (i.e., "amodal") reconstructions from arbitrary viewpoints at
    any moment in time. As an emergent property, STORM automatically captures dynamic
    instances and generates high-quality masks using only reconstruction losses. Extensive
    experiments on public datasets show that STORM achieves precise dynamic scene
    reconstruction, surpassing state-of-the-art per-scene optimization methods (+4.3
    to 6.6 PSNR) and existing feed-forward approaches (+2.1 to 4.7 PSNR) in dynamic
    regions. STORM reconstructs large-scale outdoor scenes in 200ms, supports real-time
    rendering, and outperforms competitors in scene flow estimation, improving 3D
    EPE by 0.422m and Acc5 by 28.02%. Beyond reconstruction, we showcase four additional
    applications of our model, illustrating the potential of self-supervised learning
    for broader dynamic scene understanding.
  project_page: null
  paper: https://arxiv.org/pdf/2501.00602.pdf
  code: null
  video: https://jiawei-yang.github.io/STORM/
  tags:
  - Autonomous Driving
  - Dynamic
  - Large-Scale
  - Video
  thumbnail: assets/thumbnails/yang2024storm.jpg
  publication_date: '2024-12-31T18:59:58+00:00'
- id: mao2024dreamdrive
  title: 'DreamDrive: Generative 4D Scene Modeling from Street View Images'
  authors: Jiageng Mao, Boyi Li, Boris Ivanovic, Yuxiao Chen, Yan Wang, Yurong You,
    Chaowei Xiao, Danfei Xu, Marco Pavone, Yue Wang
  year: '2024'
  abstract: Synthesizing photo-realistic visual observations from an ego vehicle's
    driving trajectory is a critical step towards scalable training of self-driving
    models. Reconstruction-based methods create 3D scenes from driving logs and synthesize
    geometry-consistent driving videos through neural rendering, but their dependence
    on costly object annotations limits their ability to generalize to in-the-wild
    driving scenarios. On the other hand, generative models can synthesize action-conditioned
    driving videos in a more generalizable way but often struggle with maintaining
    3D visual consistency. In this paper, we present DreamDrive, a 4D spatial-temporal
    scene generation approach that combines the merits of generation and reconstruction,
    to synthesize generalizable 4D driving scenes and dynamic driving videos with
    3D consistency. Specifically, we leverage the generative power of video diffusion
    models to synthesize a sequence of visual references and further elevate them
    to 4D with a novel hybrid Gaussian representation. Given a driving trajectory,
    we then render 3D-consistent driving videos via Gaussian splatting. The use of
    generative priors allows our method to produce high-quality 4D scenes from in-the-wild
    driving data, while neural rendering ensures 3D-consistent video generation from
    the 4D scenes. Extensive experiments on nuScenes and street view images demonstrate
    that DreamDrive can generate controllable and generalizable 4D driving scenes,
    synthesize novel views of driving videos with high fidelity and 3D consistency,
    decompose static and dynamic elements in a self-supervised manner, and enhance
    perception and planning tasks for autonomous driving.
  project_page: null
  paper: https://arxiv.org/pdf/2501.00601.pdf
  code: null
  video: null
  tags:
  - Autonomous Driving
  - Dynamic
  - Feed-Forward
  thumbnail: assets/thumbnails/mao2024dreamdrive.jpg
  publication_date: '2024-12-31T18:59:57+00:00'
- id: wang2024sgsplatting
  title: 'SG-Splatting: Accelerating 3D Gaussian Splatting with Spherical Gaussians'
  authors: Yiwen Wang, Siyuan Chen, Ran Yi
  year: '2024'
  abstract: '3D Gaussian Splatting is emerging as a state-of-the-art technique in
    novel view synthesis, recognized for its impressive balance between visual quality,
    speed, and rendering efficiency. However, reliance on third-degree spherical harmonics
    for color representation introduces significant storage demands and computational
    overhead, resulting in a large memory footprint and slower rendering speed. We
    introduce SG-Splatting with Spherical Gaussians based color representation, a
    novel approach to enhance rendering speed and quality in novel view synthesis.
    Our method first represents view-dependent color using Spherical Gaussians, instead
    of three degree spherical harmonics, which largely reduces the number of parameters
    used for color representation, and significantly accelerates the rendering process.
    We then develop an efficient strategy for organizing multiple Spherical Gaussians,
    optimizing their arrangement to achieve a balanced and accurate scene representation.
    To further improve rendering quality, we propose a mixed representation that combines
    Spherical Gaussians with low-degree spherical harmonics, capturing both high-
    and low-frequency color information effectively. SG-Splatting also has plug-and-play
    capability, allowing it to be easily integrated into existing systems. This approach
    improves computational efficiency and overall visual fidelity, making it a practical
    solution for real-time applications.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2501.00342.pdf
  code: null
  video: null
  tags:
  - Acceleration
  thumbnail: assets/thumbnails/wang2024sgsplatting.jpg
  publication_date: '2024-12-31T08:31:52+00:00'
- id: cha2024perse
  title: 'PERSE: Personalized 3D Generative Avatars from A Single Portrait'
  authors: Hyunsoo Cha, Inhee Lee, Hanbyul Joo
  year: '2024'
  abstract: We present PERSE, a method for building an animatable personalized generative
    avatar from a reference portrait. Our avatar model enables facial attribute editing
    in a continuous and disentangled latent space to control each facial attribute,
    while preserving the individual's identity. To achieve this, our method begins
    by synthesizing large-scale synthetic 2D video datasets, where each video contains
    consistent changes in the facial expression and viewpoint, combined with a variation
    in a specific facial attribute from the original input. We propose a novel pipeline
    to produce high-quality, photorealistic 2D videos with facial attribute editing.
    Leveraging this synthetic attribute dataset, we present a personalized avatar
    creation method based on the 3D Gaussian Splatting, learning a continuous and
    disentangled latent space for intuitive facial attribute manipulation. To enforce
    smooth transitions in this latent space, we introduce a latent space regularization
    technique by using interpolated 2D faces as supervision. Compared to previous
    approaches, we demonstrate that PERSE generates high-quality avatars with interpolated
    attributes while preserving identity of reference person.
  project_page: https://hyunsoocha.github.io/perse/
  paper: https://arxiv.org/pdf/2412.21206v1.pdf
  code: null
  video: https://youtu.be/zX881Zx03o4
  tags:
  - Avatar
  - GAN
  - Project
  - Video
  thumbnail: assets/thumbnails/cha2024perse.jpg
  publication_date: '2024-12-30T18:59:58+00:00'
- id: yang20244d
  title: '4D Gaussian Splatting: Modeling Dynamic Scenes with Native 4D Primitives'
  authors: Zeyu Yang, Zijie Pan, Xiatian Zhu, Li Zhang, Yu-Gang Jiang, Philip H. S.
    Torr
  year: '2024'
  abstract: Dynamic 3D scene representation and novel view synthesis from captured
    videos are crucial for enabling immersive experiences required by AR/VR and metaverse
    applications. However, this task is challenging due to the complexity of unconstrained
    real-world scenes and their temporal dynamics. In this paper, we frame dynamic
    scenes as a spatio-temporal 4D volume learning problem, offering a native explicit
    reformulation with minimal assumptions about motion, which serves as a versatile
    dynamic scene learning framework. Specifically, we represent a target dynamic
    scene using a collection of 4D Gaussian primitives with explicit geometry and
    appearance features, dubbed as 4D Gaussian splatting (4DGS). This approach can
    capture relevant information in space and time by fitting the underlying spatio-temporal
    volume. Modeling the spacetime as a whole with 4D Gaussians parameterized by anisotropic
    ellipses that can rotate arbitrarily in space and time, our model can naturally
    learn view-dependent and time-evolved appearance with 4D spherindrical harmonics.
    Notably, our 4DGS model is the first solution that supports real-time rendering
    of high-resolution, photorealistic novel views for complex dynamic scenes. To
    enhance efficiency, we derive several compact variants that effectively reduce
    memory footprint and mitigate the risk of overfitting. Extensive experiments validate
    the superiority of 4DGS in terms of visual quality and efficiency across a range
    of dynamic scene-related tasks (e.g., novel view synthesis, 4D generation, scene
    understanding) and scenarios (e.g., single object, indoor scenes, driving environments,
    synthetic and real data).
  project_page: null
  paper: https://arxiv.org/pdf/2412.20720v1.pdf
  code: null
  video: null
  tags:
  - Compression
  - Dynamic
  - Large-Scale
  thumbnail: assets/thumbnails/yang20244d.jpg
  publication_date: '2024-12-30T05:30:26+00:00'
- id: liu2024maskgaussian
  title: 'MaskGaussian: Adaptive 3D Gaussian Representation from Probabilistic Masks'
  authors: Yifei Liu, Zhihang Zhong, Yifan Zhan, Sheng Xu, Xiao Sun
  year: '2024'
  abstract: 'While 3D Gaussian Splatting (3DGS) has demonstrated remarkable performance
    in novel view synthesis and real-time rendering, the high memory consumption due
    to the use of millions of Gaussians limits its practicality. To mitigate this
    issue, improvements have been made by pruning unnecessary Gaussians, either through
    a hand-crafted criterion or by using learned masks. However, these methods deterministically
    remove Gaussians based on a snapshot of the pruning moment, leading to sub-optimized
    reconstruction performance from a long-term perspective. To address this issue,
    we introduce MaskGaussian, which models Gaussians as probabilistic entities rather
    than permanently removing them, and utilize them according to their probability
    of existence. To achieve this, we propose a masked-rasterization technique that
    enables unused yet probabilistically existing Gaussians to receive gradients,
    allowing for dynamic assessment of their contribution to the evolving scene and
    adjustment of their probability of existence. Hence, the importance of Gaussians
    iteratively changes and the pruned Gaussians are selected diversely. Extensive
    experiments demonstrate the superiority of the proposed method in achieving better
    rendering quality with fewer Gaussians than previous pruning methods, pruning
    over 60% of Gaussians on average with only a 0.02 PSNR decline. Our code can be
    found at: https://github.com/kaikai23/MaskGaussian

    '
  project_page: null
  paper: https://arxiv.org/pdf/2412.20522.pdf
  code: https://github.com/kaikai23/MaskGaussian
  video: null
  tags:
  - Code
  - Compression
  - Densification
  thumbnail: assets/thumbnails/liu2024maskgaussian.jpg
  publication_date: '2024-12-29T17:12:16+00:00'
  date_source: arxiv
- id: zeller2024gsplatloc
  title: 'GSplatLoc: Ultra-Precise Camera Localization via 3D Gaussian Splatting'
  authors: Atticus J. Zeller
  year: '2024'
  abstract: 'We present GSplatLoc, a camera localization method that leverages the
    differentiable rendering capabilities of 3D Gaussian splatting for ultra-precise
    pose estimation. By formulating pose estimation as a gradient-based optimization
    problem that minimizes discrepancies between rendered depth maps from a pre-existing
    3D Gaussian scene and observed depth images, GSplatLoc achieves translational
    errors within 0.01 cm and near-zero rotational errors on the Replica dataset -
    significantly outperforming existing methods. Evaluations on the Replica and TUM
    RGB-D datasets demonstrate the method''s robustness in challenging indoor environments
    with complex camera motions. GSplatLoc sets a new benchmark for localization in
    dense mapping, with important implications for applications requiring accurate
    real-time localization, such as robotics and augmented reality.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2412.20056.pdf
  code: https://github.com/AtticusZeller/GsplatLoc
  video: null
  tags:
  - Code
  - In the Wild
  - Point Cloud
  - Poses
  - Robotics
  - SLAM
  thumbnail: assets/thumbnails/zeller2024gsplatloc.jpg
  publication_date: '2024-12-28T07:14:14+00:00'
  date_source: arxiv
- id: xu2024das3r
  title: 'DAS3R: Dynamics-Aware Gaussian Splatting for Static Scene Reconstruction'
  authors: Kai Xu, Tze Ho Elden Tse, Jizong Peng, Angela Yao
  year: '2024'
  abstract: 'We propose a novel framework for scene decomposition and static background
    reconstruction from everyday videos. By integrating the trained motion masks and
    modeling the static scene as Gaussian splats with dynamics-aware optimization,
    our method achieves more accurate background reconstruction results than previous
    works. Our proposed method is termed DAS3R, an abbreviation for Dynamics-Aware
    Gaussian Splatting for Static Scene Reconstruction. Compared to existing methods,
    DAS3R is more robust in complex motion scenarios, capable of handling videos where
    dynamic objects occupy a significant portion of the scene, and does not require
    camera pose inputs or point cloud data from SLAM-based methods. We compared DAS3R
    against recent distractor-free approaches on the DAVIS and Sintel datasets; DAS3R
    demonstrates enhanced performance and robustness with a margin of more than 2
    dB in PSNR. The project''s webpage can be accessed via \url{https://kai422.github.io/DAS3R/}

    '
  project_page: https://kai422.github.io/DAS3R/
  paper: https://arxiv.org/pdf/2412.19584.pdf
  code: https://github.com/kai422/das3r
  video: https://kai422.github.io/DAS3R/assets/davis.gif
  tags:
  - Code
  - Project
  - Video
  thumbnail: assets/thumbnails/xu2024das3r.jpg
  publication_date: '2024-12-27T10:59:46+00:00'
  date_source: arxiv
- id: cai2024dust
  title: 'Dust to Tower: Coarse-to-Fine Photo-Realistic Scene Reconstruction from
    Sparse Uncalibrated Images'
  authors: Xudong Cai, Yongcai Wang, Zhaoxin Fan, Deng Haoran, Shuo Wang, Wanting
    Li, Deying Li, Lun Luo, Minhang Wang, Jintao Xu
  year: '2024'
  abstract: Photo-realistic scene reconstruction from sparse-view, uncalibrated images
    is highly required in practice. Although some successes have been made, existing
    methods are either Sparse-View but require accurate camera parameters (i.e., intrinsic
    and extrinsic), or SfM-free but need densely captured images. To combine the advantages
    of both methods while addressing their respective weaknesses, we propose Dust
    to Tower (D2T), an accurate and efficient coarse-to-fine framework to optimize
    3DGS and image poses simultaneously from sparse and uncalibrated images. Our key
    idea is to first construct a coarse model efficiently and subsequently refine
    it using warped and inpainted images at novel viewpoints. To do this, we first
    introduce a Coarse Construction Module (CCM) which exploits a fast Multi-View
    Stereo model to initialize a 3D Gaussian Splatting (3DGS) and recover initial
    camera poses. To refine the 3D model at novel viewpoints, we propose a Confidence
    Aware Depth Alignment (CADA) module to refine the coarse depth maps by aligning
    their confident parts with estimated depths by a Mono-depth model. Then, a Warped
    Image-Guided Inpainting (WIGI) module is proposed to warp the training images
    to novel viewpoints by the refined depth maps, and inpainting is applied to fulfill
    the ``holes" in the warped images caused by view-direction changes, providing
    high-quality supervision to further optimize the 3D model and the camera poses.
    Extensive experiments and ablation studies demonstrate the validity of D2T and
    its design choices, achieving state-of-the-art performance in both tasks of novel
    view synthesis and pose estimation while keeping high efficiency. Codes will be
    publicly available.
  project_page: null
  paper: https://arxiv.org/pdf/2412.19518.pdf
  code: null
  video: null
  tags:
  - Inpainting
  - Poses
  - Sparse
  thumbnail: assets/thumbnails/cai2024dust.jpg
  publication_date: '2024-12-27T08:19:34+00:00'
- id: yao2024reflective
  title: Reflective Gaussian Splatting
  authors: Yuxuan Yao, Zixuan Zeng, Chun Gu, Xiatian Zhu, Li Zhang
  year: '2024'
  abstract: 'Novel view synthesis has experienced significant advancements owing to
    increasingly capable NeRF- and 3DGS-based methods. However, reflective object
    reconstruction remains challenging, lacking a proper solution to achieve real-time,
    high-quality rendering while accommodating inter-reflection. To fill this gap,
    we introduce a Reflective Gaussian splatting (\textbf{Ref-Gaussian}) framework
    characterized with two components: (I) {\em Physically based deferred rendering}
    that empowers the rendering equation with pixel-level material properties via
    formulating split-sum approximation; (II) {\em Gaussian-grounded inter-reflection}
    that realizes the desired inter-reflection function within a Gaussian splatting
    paradigm for the first time. To enhance geometry modeling, we further introduce
    material-aware normal propagation and an initial per-Gaussian shading stage, along
    with 2D Gaussian primitives. Extensive experiments on standard datasets demonstrate
    that Ref-Gaussian surpasses existing approaches in terms of quantitative metrics,
    visual quality, and compute efficiency. Further, we show that our method serves
    as a unified solution for both reflective and non-reflective scenes, going beyond
    the previous alternatives focusing on only reflective scenes. Also, we illustrate
    that Ref-Gaussian supports more applications such as relighting and editing.

    '
  project_page: https://fudan-zvg.github.io/ref-gaussian/
  paper: https://arxiv.org/pdf/2412.19282.pdf
  code: null
  video: null
  tags:
  - Meshing
  - Project
  - Ray Tracing
  - Relight
  thumbnail: assets/thumbnails/yao2024reflective.jpg
  publication_date: '2024-12-26T16:58:35+00:00'
- id: qian2024weathergs
  title: 'WeatherGS: 3D Scene Reconstruction in Adverse Weather Conditions via Gaussian
    Splatting'
  authors: Chenghao Qian, Yuhu Guo, Wenjing Li, Gustav Markkula
  year: '2024'
  abstract: 3D Gaussian Splatting (3DGS) has gained significant attention for 3D scene
    reconstruction, but still suffers from complex outdoor environments, especially
    under adverse weather. This is because 3DGS treats the artifacts caused by adverse
    weather as part of the scene and will directly reconstruct them, largely reducing
    the clarity of the reconstructed scene. To address this challenge, we propose
    WeatherGS, a 3DGS-based framework for reconstructing clear scenes from multi-view
    images under different weather conditions. Specifically, we explicitly categorize
    the multi-weather artifacts into the dense particles and lens occlusions that
    have very different characters, in which the former are caused by snowflakes and
    raindrops in the air, and the latter are raised by the precipitation on the camera
    lens. In light of this, we propose a dense-to-sparse preprocess strategy, which
    sequentially removes the dense particles by an Atmospheric Effect Filter (AEF)
    and then extracts the relatively sparse occlusion masks with a Lens Effect Detector
    (LED). Finally, we train a set of 3D Gaussians by the processed images and generated
    masks for excluding occluded areas, and accurately recover the underlying clear
    scene by Gaussian splatting. We conduct a diverse and challenging benchmark to
    facilitate the evaluation of 3D reconstruction under complex weather scenarios.
    Extensive experiments on this benchmark demonstrate that our WeatherGS consistently
    produces high-quality, clean scenes across various weather scenarios, outperforming
    existing state-of-the-art methods.
  project_page: null
  paper: https://arxiv.org/pdf/2412.18862.pdf
  code: https://github.com/Jumponthemoon/WeatherGS
  video: null
  tags:
  - Code
  - In the Wild
  thumbnail: assets/thumbnails/qian2024weathergs.jpg
  publication_date: '2024-12-25T10:16:57+00:00'
- id: lyu2024facelift
  title: 'FaceLift: Single Image to 3D Head with View Generation and GS-LRM'
  authors: Weijie Lyu, Yi Zhou, Ming-Hsuan Yang, Zhixin Shu
  year: '2024'
  abstract: We present FaceLift, a feed-forward approach for rapid, high-quality,
    360-degree head reconstruction from a single image. Our pipeline begins by employing
    a multi-view latent diffusion model that generates consistent side and back views
    of the head from a single facial input. These generated views then serve as input
    to a GS-LRM reconstructor, which produces a comprehensive 3D representation using
    Gaussian splats. To train our system, we develop a dataset of multi-view renderings
    using synthetic 3D human head as-sets. The diffusion-based multi-view generator
    is trained exclusively on synthetic head images, while the GS-LRM reconstructor
    undergoes initial training on Objaverse followed by fine-tuning on synthetic head
    data. FaceLift excels at preserving identity and maintaining view consistency
    across views. Despite being trained solely on synthetic data, FaceLift demonstrates
    remarkable generalization to real-world images. Through extensive qualitative
    and quantitative evaluations, we show that FaceLift outperforms state-of-the-art
    methods in 3D head reconstruction, highlighting its practical applicability and
    robust performance on real-world images. In addition to single image reconstruction,
    FaceLift supports video inputs for 4D novel view synthesis and seamlessly integrates
    with 2D reanimation techniques to enable 3D facial animation.
  project_page: https://www.wlyu.me/FaceLift/
  paper: https://arxiv.org/pdf/2412.17812.pdf
  code: null
  video: https://huggingface.co/wlyu/FaceLift/resolve/main/videos/website_video.mp4
  tags:
  - Avatar
  - Feed-Forward
  - Project
  - Video
  thumbnail: assets/thumbnails/lyu2024facelift.jpg
  publication_date: '2024-12-23T18:59:49+00:00'
- id: shao2024gausim
  title: 'GauSim: Registering Elastic Objects into Digital World by Gaussian Simulator'
  authors: Yidi Shao, Mu Huang, Chen Change Loy, Bo Dai
  year: '2024'
  abstract: In this work, we introduce GauSim, a novel neural network-based simulator
    designed to capture the dynamic behaviors of real-world elastic objects represented
    through Gaussian kernels. Unlike traditional methods that treat kernels as particles
    within particle-based simulations, we leverage continuum mechanics, modeling each
    kernel as a continuous piece of matter to account for realistic deformations without
    idealized assumptions. To improve computational efficiency and fidelity, we employ
    a hierarchical structure that organizes kernels into Center of Mass Systems (CMS)
    with explicit formulations, enabling a coarse-to-fine simulation approach. This
    structure significantly reduces computational overhead while preserving detailed
    dynamics. In addition, GauSim incorporates explicit physics constraints, such
    as mass and momentum conservation, ensuring interpretable results and robust,
    physically plausible simulations. To validate our approach, we present a new dataset,
    READY, containing multi-view videos of real-world elastic deformations. Experimental
    results demonstrate that GauSim achieves superior performance compared to existing
    physics-driven baselines, offering a practical and accurate solution for simulating
    complex dynamic behaviors. Code and model will be released.
  project_page: https://www.mmlab-ntu.com/project/gausim/index.html
  paper: https://arxiv.org/pdf/2412.17804.pdf
  code: null
  video: null
  tags:
  - Dynamic
  - Physics
  - Project
  thumbnail: assets/thumbnails/shao2024gausim.jpg
  publication_date: '2024-12-23T18:58:17+00:00'
- id: jin2024activegs
  title: 'ActiveGS: Active Scene Reconstruction using Gaussian Splatting'
  authors: Liren Jin, Xingguang Zhong, Yue Pan, Jens Behley, Cyrill Stachniss, Marija
    Popović
  year: '2024'
  abstract: 'Robotics applications often rely on scene reconstructions to enable downstream
    tasks. In this work, we tackle the challenge of actively building an accurate
    map of an unknown scene using an on-board RGB-D camera. We propose a hybrid map
    representation that combines a Gaussian splatting map with a coarse voxel map,
    leveraging the strengths of both representations: the high-fidelity scene reconstruction
    capabilities of Gaussian splatting and the spatial modelling strengths of the
    voxel map. The core of our framework is an effective confidence modelling technique
    for the Gaussian splatting map to identify under-reconstructed areas, while utilising
    spatial information from the voxel map to target unexplored areas and assist in
    collision-free path planning. By actively collecting scene information in under-reconstructed
    and unexplored areas for map updates, our approach achieves superior Gaussian
    splatting reconstruction results compared to state-of-the-art approaches. Additionally,
    we demonstrate the applicability of our active scene reconstruction framework
    in the real world using an unmanned aerial vehicle.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2412.17769.pdf
  code: null
  video: null
  tags:
  - Meshing
  - Robotics
  - SLAM
  thumbnail: assets/thumbnails/jin2024activegs.jpg
  publication_date: '2024-12-23T18:29:03+00:00'
  date_source: arxiv
- id: gao2024cosurfgscollaborative
  title: CoSurfGS:Collaborative 3D Surface Gaussian Splatting with Distributed Learning
    for Large Scene Reconstruction
  authors: Yuanyuan Gao, Yalun Dai, Hao Li, Weicai Ye, Junyi Chen, Danpeng Chen, Dingwen
    Zhang, Tong He, Guofeng Zhang, Junwei Han
  year: '2024'
  abstract: 3D Gaussian Splatting (3DGS) has demonstrated impressive performance in
    scene reconstruction. However, most existing GS-based surface reconstruction methods
    focus on 3D objects or limited scenes. Directly applying these methods to large-scale
    scene reconstruction will pose challenges such as high memory costs, excessive
    time consumption, and lack of geometric detail, which makes it difficult to implement
    in practical applications. To address these issues, we propose a multi-agent collaborative
    fast 3DGS surface reconstruction framework based on distributed learning for large-scale
    surface reconstruction. Specifically, we develop local model compression (LMC)
    and model aggregation schemes (MAS) to achieve high-quality surface representation
    of large scenes while reducing GPU memory consumption. Extensive experiments on
    Urban3d, MegaNeRF, and BlendedMVS demonstrate that our proposed method can achieve
    fast and scalable high-fidelity surface reconstruction and photorealistic rendering.
  project_page: https://gyy456.github.io/CoSurfGS/
  paper: https://arxiv.org/pdf/2412.17612.pdf
  code: null
  video: null
  tags:
  - Distributed
  - Large-Scale
  - Meshing
  - Project
  thumbnail: assets/thumbnails/gao2024cosurfgscollaborative.jpg
  publication_date: '2024-12-23T14:31:15+00:00'
- id: gui2024balanced
  title: 'Balanced 3DGS: Gaussian-wise Parallelism Rendering with Fine-Grained Tiling'
  authors: Hao Gui, Lin Hu, Rui Chen, Mingxiao Huang, Yuxin Yin, Jin Yang, Yong Wu
  year: '2024'
  abstract: '3D Gaussian Splatting (3DGS) is increasingly attracting attention in
    both academia and industry owing to its superior visual quality and rendering
    speed. However, training a 3DGS model remains a time-intensive task, especially
    in load imbalance scenarios where workload diversity among pixels and Gaussian
    spheres causes poor renderCUDA kernel performance. We introduce Balanced 3DGS,
    a Gaussian-wise parallelism rendering with fine-grained tiling approach in 3DGS
    training process, perfectly solving load-imbalance issues. First, we innovatively
    introduce the inter-block dynamic workload distribution technique to map workloads
    to Streaming Multiprocessor(SM) resources within a single GPU dynamically, which
    constitutes the foundation of load balancing. Second, we are the first to propose
    the Gaussian-wise parallel rendering technique to significantly reduce workload
    divergence inside a warp, which serves as a critical component in addressing load
    imbalance. Based on the above two methods, we further creatively put forward the
    fine-grained combined load balancing technique to uniformly distribute workload
    across all SMs, which boosts the forward renderCUDA kernel performance by up to
    7.52x. Besides, we present a self-adaptive render kernel selection strategy during
    the 3DGS training process based on different load-balance situations, which effectively
    improves training efficiency.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2412.17378.pdf
  code: null
  video: null
  tags:
  - Acceleration
  thumbnail: assets/thumbnails/gui2024balanced.jpg
  publication_date: '2024-12-23T08:26:30+00:00'
- id: jambon2024interactive
  title: Interactive Scene Authoring with Specialized Generative Primitives
  authors: Clément Jambon, Changwoon Choi, Dongsu Zhang, Olga Sorkine-Hornung, Young
    Min Kim
  year: '2024'
  abstract: 'Generating high-quality 3D digital assets often requires expert knowledge
    of complex design tools. We introduce Specialized Generative Primitives, a generative
    framework that allows non-expert users to author high-quality 3D scenes in a seamless,
    lightweight, and controllable manner. Each primitive is an efficient generative
    model that captures the distribution of a single exemplar from the real world.
    With our framework, users capture a video of an environment, which we turn into
    a high-quality and explicit appearance model thanks to 3D Gaussian Splatting.
    Users then select regions of interest guided by semantically-aware features. To
    create a generative primitive, we adapt Generative Cellular Automata to single-exemplar
    training and controllable generation. We decouple the generative task from the
    appearance model by operating on sparse voxels and we recover a high-quality output
    with a subsequent sparse patch consistency step. Each primitive can be trained
    within 10 minutes and used to author new scenes interactively in a fully compositional
    manner. We showcase interactive sessions where various primitives are extracted
    from real-world scenes and controlled to create 3D assets and scenes in a few
    minutes. We also demonstrate additional capabilities of our primitives: handling
    various 3D representations to control generation, transferring appearances, and
    editing geometries.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2412.16253.pdf
  code: null
  video: null
  tags:
  - Editing
  - World Generation
  thumbnail: assets/thumbnails/jambon2024interactive.jpg
  publication_date: '2024-12-20T04:39:50+00:00'
- id: shen2024solidgs
  title: 'SolidGS: Consolidating Gaussian Surfel Splatting for Sparse-View Surface
    Reconstruction'
  authors: Zhuowen Shen, Yuan Liu, Zhang Chen, Zhong Li, Jiepeng Wang, Yongqing Liang,
    Zhengming Yu, Jingdong Zhang, Yi Xu, Scott Schaefer, Xin Li, Wenping Wang
  year: '2024'
  abstract: 'Gaussian splatting has achieved impressive improvements for both novel-view
    synthesis and surface reconstruction from multi-view images. However, current
    methods still struggle to reconstruct high-quality surfaces from only sparse view
    input images using Gaussian splatting. In this paper, we propose a novel method
    called SolidGS to address this problem. We observed that the reconstructed geometry
    can be severely inconsistent across multi-views, due to the property of Gaussian
    function in geometry rendering. This motivates us to consolidate all Gaussians
    by adopting a more solid kernel function, which effectively improves the surface
    reconstruction quality. With the additional help of geometrical regularization
    and monocular normal estimation, our method achieves superior performance on the
    sparse view surface reconstruction than all the Gaussian splatting methods and
    neural field methods on the widely used DTU, Tanks-and-Temples, and LLFF datasets.

    '
  project_page: https://mickshen7558.github.io/projects/SolidGS/
  paper: https://arxiv.org/pdf/2412.15400.pdf
  code: null
  video: null
  tags:
  - Meshing
  - Project
  - Sparse
  thumbnail: assets/thumbnails/shen2024solidgs.jpg
  publication_date: '2024-12-19T21:04:43+00:00'
  date_source: arxiv
- id: xie2024envgs
  title: 'EnvGS: Modeling View-Dependent Appearance with Environment Gaussian'
  authors: Tao Xie, Xi Chen, Zhen Xu, Yiman Xie, Yudong Jin, Yujun Shen, Sida Peng,
    Hujun Bao, Xiaowei Zhou
  year: '2024'
  abstract: 'Reconstructing complex reflections in real-world scenes from 2D images
    is essential for achieving photorealistic novel view synthesis. Existing methods
    that utilize environment maps to model reflections from distant lighting often
    struggle with high-frequency reflection details and fail to account for near-field
    reflections. In this work, we introduce EnvGS, a novel approach that employs a
    set of Gaussian primitives as an explicit 3D representation for capturing reflections
    of environments. These environment Gaussian primitives are incorporated with base
    Gaussian primitives to model the appearance of the whole scene. To efficiently
    render these environment Gaussian primitives, we developed a ray-tracing-based
    renderer that leverages the GPU''s RT core for fast rendering. This allows us
    to jointly optimize our model for high-quality reconstruction while maintaining
    real-time rendering speeds. Results from multiple real-world and synthetic datasets
    demonstrate that our method produces significantly more detailed reflections,
    achieving the best rendering quality in real-time novel view synthesis.

    '
  project_page: https://zju3dv.github.io/envgs/
  paper: https://arxiv.org/pdf/2412.15215.pdf
  code: null
  video: https://raw.githubusercontent.com/xbillowy/assets/refs/heads/main/envgs.github.io.assets/teaser.mp4
  tags:
  - Project
  - Ray Tracing
  - Rendering
  - Video
  thumbnail: assets/thumbnails/xie2024envgs.jpg
  publication_date: '2024-12-19T18:59:57+00:00'
  date_source: arxiv
- id: saito2024squeezeme
  title: 'SqueezeMe: Efficient Gaussian Avatars for VR'
  authors: Shunsuke Saito, Stanislav Pidhorskyi, Igor Santesteban, Forrest Iandola,
    Divam Gupta, Anuj Pahuja, Nemanja Bartolovic, Frank Yu, Emanuel Garbin, Tomas
    Simon
  year: '2024'
  abstract: "Gaussian Splatting has enabled real-time 3D human avatars with unprecedented\
    \ levels of visual quality. While previous methods require a desktop GPU for real-time\
    \ inference of a single avatar, we aim to squeeze multiple Gaussian avatars onto\
    \ a portable virtual reality headset with real-time drivable inference. We begin\
    \ by training a previous work, Animatable Gaussians, on a high quality dataset\
    \ captured with 512 cameras. The Gaussians are animated by controlling base set\
    \ of Gaussians with linear blend skinning (LBS) motion and then further adjusting\
    \ the Gaussians with a neural network decoder to correct their appearance. When\
    \ deploying the model on a Meta Quest 3 VR headset, we find two major computational\
    \ bottlenecks: the decoder and the rendering. To accelerate the decoder, we train\
    \ the Gaussians in UV-space instead of pixel-space, and we distill the decoder\
    \ to a single neural network layer. Further, we discover that neighborhoods of\
    \ Gaussians can share a single corrective from the decoder, which provides an\
    \ additional speedup. To accelerate the rendering, we develop a custom pipeline\
    \ in Vulkan that runs on the mobile GPU. Putting it all together, we run 3 Gaussian\
    \ avatars concurrently at 72 FPS on a VR headset. \n"
  project_page: https://forresti.github.io/squeezeme
  paper: https://arxiv.org/pdf/2412.15171.pdf
  code: null
  video: null
  tags:
  - Avatar
  - Dynamic
  - Project
  thumbnail: assets/thumbnails/saito2024squeezeme.jpg
  publication_date: '2024-12-19T18:46:55+00:00'
  date_source: arxiv
- id: peng2024gags
  title: 'GAGS: Granularity-Aware Feature Distillation for Language Gaussian Splatting'
  authors: Yuning Peng, Haiping Wang, Yuan Liu, Chenglu Wen, Zhen Dong, Bisheng Yang
  year: '2024'
  abstract: '3D open-vocabulary scene understanding, which accurately perceives complex
    semantic properties of objects in space, has gained significant attention in recent
    years. In this paper, we propose GAGS, a framework that distills 2D CLIP features
    into 3D Gaussian splatting, enabling open-vocabulary queries for renderings on
    arbitrary viewpoints. The main challenge of distilling 2D features for 3D fields
    lies in the multiview inconsistency of extracted 2D features, which provides unstable
    supervision for the 3D feature field. GAGS addresses this challenge with two novel
    strategies. First, GAGS associates the prompt point density of SAM with the camera
    distances, which significantly improves the multiview consistency of segmentation
    results. Second, GAGS further decodes a granularity factor to guide the distillation
    process and this granularity factor can be learned in a unsupervised manner to
    only select the multiview consistent 2D features in the distillation process.
    Experimental results on two datasets demonstrate significant performance and stability
    improvements of GAGS in visual grounding and semantic segmentation, with an inference
    speed 2$\times$ faster than baseline methods. The code and additional results
    are available at https://pz0826.github.io/GAGS-Webpage/ .

    '
  project_page: https://pz0826.github.io/GAGS-Webpage/
  paper: https://arxiv.org/pdf/2412.13654.pdf
  code: https://github.com/WHU-USI3DV/GAGS
  video: null
  tags:
  - Code
  - Language Embedding
  - Project
  - Segmentation
  thumbnail: assets/thumbnails/peng2024gags.jpg
  publication_date: '2024-12-18T09:33:20+00:00'
  date_source: arxiv
- id: lu2024turbogs
  title: 'Turbo-GS: Accelerating 3D Gaussian Fitting for High-Quality Radiance Fields'
  authors: Tao Lu, Ankit Dhiman, R Srinath, Emre Arslan, Angela Xing, Yuanbo Xiangli,
    R Venkatesh Babu, Srinath Sridhar
  year: '2024'
  abstract: 'Novel-view synthesis is an important problem in computer vision with
    applications in 3D reconstruction, mixed reality, and robotics. Recent methods
    like 3D Gaussian Splatting (3DGS) have become the preferred method for this task,
    providing high-quality novel views in real time. However, the training time of
    a 3DGS model is slow, often taking 30 minutes for a scene with 200 views. In contrast,
    our goal is to reduce the optimization time by training for fewer steps while
    maintaining high rendering quality. Specifically, we combine the guidance from
    both the position error and the appearance error to achieve a more effective densification.
    To balance the rate between adding new Gaussians and fitting old Gaussians, we
    develop a convergence-aware budget control mechanism. Moreover, to make the densification
    process more reliable, we selectively add new Gaussians from mostly visited regions.
    With these designs, we reduce the Gaussian optimization steps to one-third of
    the previous approach while achieving a comparable or even better novel view rendering
    quality. To further facilitate the rapid fitting of 4K resolution images, we introduce
    a dilation-based rendering technique. Our method, Turbo-GS, speeds up optimization
    for typical scenes and scales well to high-resolution (4K) scenarios on standard
    datasets. Through extensive experiments, we show that our method is significantly
    faster in optimization than other methods while retaining quality. Project page:
    https://ivl.cs.brown.edu/research/turbo-gs.

    '
  project_page: https://ivl.cs.brown.edu/research/turbo-gs
  paper: https://arxiv.org/pdf/2412.13547v1.pdf
  code: null
  video: null
  tags:
  - Acceleration
  - Densification
  - Project
  thumbnail: assets/thumbnails/lu2024turbogs.jpg
  publication_date: '2024-12-18T06:46:40+00:00'
  date_source: arxiv
- id: jiang2024gausstr
  title: 'GaussTR: Foundation Model-Aligned Gaussian Transformer for Self-Supervised
    3D Spatial Understanding'
  authors: Haoyi Jiang, Liu Liu, Tianheng Cheng, Xinjie Wang, Tianwei Lin, Zhizhong
    Su, Wenyu Liu, Xinggang Wang
  year: '2024'
  abstract: '3D Semantic Occupancy Prediction is fundamental for spatial understanding
    as it provides a comprehensive semantic cognition of surrounding environments.
    However, prevalent approaches primarily rely on extensive labeled data and computationally
    intensive voxel-based modeling, restricting the scalability and generalizability
    of 3D representation learning. In this paper, we introduce GaussTR, a novel Gaussian
    Transformer that leverages alignment with foundation models to advance self-supervised
    3D spatial understanding. GaussTR adopts a Transformer architecture to predict
    sparse sets of 3D Gaussians that represent scenes in a feed-forward manner. Through
    aligning rendered Gaussian features with diverse knowledge from pre-trained foundation
    models, GaussTR facilitates the learning of versatile 3D representations and enables
    open-vocabulary occupancy prediction without explicit annotations. Empirical evaluations
    on the Occ3D-nuScenes dataset showcase GaussTR''s state-of-the-art zero-shot performance,
    achieving 11.70 mIoU while reducing training duration by approximately 50%. These
    experimental results highlight the significant potential of GaussTR for scalable
    and holistic 3D spatial understanding, with promising implications for autonomous
    driving and embodied agents. Code is available at https://github.com/hustvl/GaussTR.

    '
  project_page: https://hustvl.github.io/GaussTR/
  paper: https://arxiv.org/pdf/2412.13193.pdf
  code: https://github.com/hustvl/GaussTR
  video: null
  tags:
  - Code
  - Project
  thumbnail: assets/thumbnails/jiang2024gausstr.jpg
  publication_date: '2024-12-17T18:59:46+00:00'
  date_source: arxiv
- id: sun2024realtime
  title: Real-time Free-view Human Rendering from Sparse-view RGB Videos using Double
    Unprojected Textures
  authors: Guoxing Sun, Rishabh Dabral, Heming Zhu, Pascal Fua, Christian Theobalt,
    Marc Habermann
  year: '2024'
  abstract: 'Real-time free-view human rendering from sparse-view RGB inputs is a
    challenging task due to the sensor scarcity and the tight time budget. To ensure
    efficiency, recent methods leverage 2D CNNs operating in texture space to learn
    rendering primitives. However, they either jointly learn geometry and appearance,
    or completely ignore sparse image information for geometry estimation, significantly
    harming visual quality and robustness to unseen body poses. To address these issues,
    we present Double Unprojected Textures, which at the core disentangles coarse
    geometric deformation estimation from appearance synthesis, enabling robust and
    photorealistic 4K rendering in real-time. Specifically, we first introduce a novel
    image-conditioned template deformation network, which estimates the coarse deformation
    of the human template from a first unprojected texture. This updated geometry
    is then used to apply a second and more accurate texture unprojection. The resulting
    texture map has fewer artifacts and better alignment with input views, which benefits
    our learning of finer-level geometry and appearance represented by Gaussian splats.
    We validate the effectiveness and efficiency of the proposed method in quantitative
    and qualitative experiments, which significantly surpasses other state-of-the-art
    methods.

    '
  project_page: https://vcai.mpi-inf.mpg.de/projects/DUT/
  paper: https://arxiv.org/pdf/2412.13183v1.pdf
  code: null
  video: https://vcai.mpi-inf.mpg.de/projects/DUT/videos/main_video.mp4
  tags:
  - Avatar
  - Project
  - Sparse
  - Texturing
  - Video
  thumbnail: assets/thumbnails/sun2024realtime.jpg
  publication_date: '2024-12-17T18:57:38+00:00'
  date_source: arxiv
- id: weiss2024gaussian
  title: 'Gaussian Billboards: Expressive 2D Gaussian Splatting with Textures'
  authors: Sebastian Weiss, Derek Bradley
  year: '2024'
  abstract: 'Gaussian Splatting has recently emerged as the go-to representation for
    reconstructing and rendering 3D scenes. The transition from 3D to 2D Gaussian
    primitives has further improved multi-view consistency and surface reconstruction
    accuracy. In this work we highlight the similarity between 2D Gaussian Splatting
    (2DGS) and billboards from traditional computer graphics. Both use flat semi-transparent
    2D geometry that is positioned, oriented and scaled in 3D space. However 2DGS
    uses a solid color per splat and an opacity modulated by a Gaussian distribution,
    where billboards are more expressive, modulating the color with a uv-parameterized
    texture. We propose to unify these concepts by presenting Gaussian Billboards,
    a modification of 2DGS to add spatially-varying color achieved using per-splat
    texture interpolation. The result is a mixture of the two representations, which
    benefits from both the robust scene optimization power of 2DGS and the expressiveness
    of texture mapping. We show that our method can improve the sharpness and quality
    of the scene representation in a wide range of qualitative and quantitative evaluations
    compared to the original 2DGS implementation.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2412.12734v1.pdf
  code: null
  video: null
  tags:
  - 2DGS
  - Texturing
  thumbnail: assets/thumbnails/weiss2024gaussian.jpg
  publication_date: '2024-12-17T09:57:04+00:00'
  date_source: arxiv
- id: wu20243dgut
  title: '3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting'
  authors: Qi Wu, Janick Martinez Esturo, Ashkan Mirzaei, Nicolas Moenne-Loccoz, Zan
    Gojcic
  year: '2024'
  abstract: '3D Gaussian Splatting (3DGS) has shown great potential for efficient
    reconstruction and high-fidelity real-time rendering of complex scenes on consumer
    hardware. However, due to its rasterization-based formulation, 3DGS is constrained
    to ideal pinhole cameras and lacks support for secondary lighting effects. Recent
    methods address these limitations by tracing volumetric particles instead, however,
    this comes at the cost of significantly slower rendering speeds. In this work,
    we propose 3D Gaussian Unscented Transform (3DGUT), replacing the EWA splatting
    formulation in 3DGS with the Unscented Transform that approximates the particles
    through sigma points, which can be projected exactly under any nonlinear projection
    function. This modification enables trivial support of distorted cameras with
    time dependent effects such as rolling shutter, while retaining the efficiency
    of rasterization. Additionally, we align our rendering formulation with that of
    tracing-based methods, enabling secondary ray tracing required to represent phenomena
    such as reflections and refraction within the same 3D representation.

    '
  project_page: https://research.nvidia.com/labs/toronto-ai/3DGUT/
  paper: https://arxiv.org/pdf/2412.12507.pdf
  code: null
  video: https://research.nvidia.com/labs/toronto-ai/3DGUT/res/3DGUT_ready_compressed.mp4
  tags:
  - Perspective-correct
  - Project
  - Video
  thumbnail: assets/thumbnails/wu20243dgut.jpg
  publication_date: '2024-12-17T03:21:25+00:00'
  date_source: arxiv
- id: murai2024mast3rslam
  title: 'MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors'
  authors: Riku Murai, Eric Dexheimer, Andrew J. Davison
  year: '2024'
  abstract: 'We present a real-time monocular dense SLAM system designed bottom-up
    from MASt3R, a two-view 3D reconstruction and matching prior. Equipped with this
    strong prior, our system is robust on in-the-wild video sequences despite making
    no assumption on a fixed or parametric camera model beyond a unique camera centre.
    We introduce efficient methods for pointmap matching, camera tracking and local
    fusion, graph construction and loop closure, and second-order global optimisation.
    With known calibration, a simple modification to the system achieves state-of-the-art
    performance across various benchmarks. Altogether, we propose a plug-and-play
    monocular SLAM system capable of producing globally-consistent poses and dense
    geometry while operating at 15 FPS.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2412.12392.pdf
  code: null
  video: https://www.youtube.com/watch?v=wozt71NBFTQ
  tags:
  - 3ster-based
  - SLAM
  - Video
  thumbnail: assets/thumbnails/murai2024mast3rslam.jpg
  publication_date: '2024-12-16T23:00:05+00:00'
  date_source: arxiv
- id: zhang2024pansplat
  title: 'PanSplat: 4K Panorama Synthesis with Feed-Forward Gaussian Splatting'
  authors: Cheng Zhang, Haofei Xu, Qianyi Wu, Camilo Cruz Gambardella, Dinh Phung,
    Jianfei Cai
  year: '2024'
  abstract: 'With the advent of portable 360{\deg} cameras, panorama has gained significant
    attention in applications like virtual reality (VR), virtual tours, robotics,
    and autonomous driving. As a result, wide-baseline panorama view synthesis has
    emerged as a vital task, where high resolution, fast inference, and memory efficiency
    are essential. Nevertheless, existing methods are typically constrained to lower
    resolutions (512 $\times$ 1024) due to demanding memory and computational requirements.
    In this paper, we present PanSplat, a generalizable, feed-forward approach that
    efficiently supports resolution up to 4K (2048 $\times$ 4096). Our approach features
    a tailored spherical 3D Gaussian pyramid with a Fibonacci lattice arrangement,
    enhancing image quality while reducing information redundancy. To accommodate
    the demands of high resolution, we propose a pipeline that integrates a hierarchical
    spherical cost volume and Gaussian heads with local operations, enabling two-step
    deferred backpropagation for memory-efficient training on a single A100 GPU. Experiments
    demonstrate that PanSplat achieves state-of-the-art results with superior efficiency
    and image quality across both synthetic and real-world datasets. Code will be
    available at \url{https://github.com/chengzhag/PanSplat}.

    '
  project_page: https://chengzhag.github.io/publication/pansplat/
  paper: https://arxiv.org/pdf/2412.12096v1.pdf
  code: https://github.com/chengzhag/PanSplat
  video: https://youtu.be/R3qIzL77ZSc
  tags:
  - 360 degree
  - Code
  - Feed-Forward
  - Project
  - Video
  - World Generation
  thumbnail: assets/thumbnails/zhang2024pansplat.jpg
  publication_date: '2024-12-16T18:59:45+00:00'
  date_source: arxiv
- id: taubner2024cap4d
  title: 'CAP4D: Creating Animatable 4D Portrait Avatars with Morphable Multi-View
    Diffusion Models'
  authors: Felix Taubner, Ruihang Zhang, Mathieu Tuli, David B. Lindell
  year: '2024'
  abstract: 'Reconstructing photorealistic and dynamic portrait avatars from images
    is essential to many applications including advertising, visual effects, and virtual
    reality. Depending on the application, avatar reconstruction involves different
    capture setups and constraints − for example, visual effects studios use camera
    arrays to capture hundreds of reference images, while content creators may seek
    to animate a single portrait image downloaded from the internet. As such, there
    is a large and heterogeneous ecosystem of methods for avatar reconstruction. Techniques
    based on multi-view stereo or neural rendering achieve the highest quality results,
    but require hundreds of reference images. Recent generative models produce convincing
    avatars from a single reference image, but visual fidelity yet lags behind multi-view
    techniques. Here, we present CAP4D: an approach that uses a morphable multi-view
    diffusion model to reconstruct photoreal 4D (dynamic 3D) portrait avatars from
    any number of reference images (i.e., one to 100) and animate and render them
    in real time. Our approach demonstrates state-of-the-art performance for single-,
    few-, and multi-image 4D portrait avatar reconstruction, and takes steps to bridge
    the gap in visual fidelity between single-image and multi-view reconstruction
    techniques.'
  project_page: https://felixtaubner.github.io/cap4d/
  paper: https://arxiv.org/pdf/2412.12093
  code: null
  video: null
  tags:
  - Avatar
  - Project
  thumbnail: assets/thumbnails/taubner2024cap4d.jpg
  publication_date: '2024-12-16T18:58:51+00:00'
- id: liang2024wonderland
  title: 'Wonderland: Navigating 3D Scenes from a Single Image'
  authors: Hanwen Liang, Junli Cao, Vidit Goel, Guocheng Qian, Sergei Korolev, Demetri
    Terzopoulos, Konstantinos N. Plataniotis, Sergey Tulyakov, Jian Ren
  year: '2024'
  abstract: 'This paper addresses a challenging question: How can we efficiently create
    high-quality, wide-scope 3D scenes from a single arbitrary image? Existing methods
    face several constraints, such as requiring multi-view data, time-consuming per-scene
    optimization, low visual quality in backgrounds, and distorted reconstructions
    in unseen areas. We propose a novel pipeline to overcome these limitations. Specifically,
    we introduce a large-scale reconstruction model that uses latents from a video
    diffusion model to predict 3D Gaussian Splattings for the scenes in a feed-forward
    manner. The video diffusion model is designed to create videos precisely following
    specified camera trajectories, allowing it to generate compressed video latents
    that contain multi-view information while maintaining 3D consistency. We train
    the 3D reconstruction model to operate on the video latent space with a progressive
    training strategy, enabling the efficient generation of high-quality, wide-scope,
    and generic 3D scenes. Extensive evaluations across various datasets demonstrate
    that our model significantly outperforms existing methods for single-view 3D scene
    generation, particularly with out-of-domain images. For the first time, we demonstrate
    that a 3D reconstruction model can be effectively built upon the latent space
    of a diffusion model to realize efficient 3D scene generation.

    '
  project_page: https://snap-research.github.io/wonderland/
  paper: https://arxiv.org/pdf/2412.12091v1.pdf
  code: null
  video: null
  tags:
  - Feed-Forward
  - Project
  - Sparse
  - World Generation
  thumbnail: assets/thumbnails/liang2024wonderland.jpg
  publication_date: '2024-12-16T18:58:17+00:00'
  date_source: arxiv
- id: huang2024deformable
  title: Deformable Radial Kernel Splatting
  authors: Yi-Hua Huang, Ming-Xian Lin, Yang-Tian Sun, Ziyi Yang, Xiaoyang Lyu, Yan-Pei
    Cao, Xiaojuan Qi
  year: '2024'
  abstract: 'Recently, Gaussian splatting has emerged as a robust technique for representing
    3D scenes, enabling real-time rasterization and high-fidelity rendering. However,
    Gaussians'' inherent radial symmetry and smoothness constraints limit their ability
    to represent complex shapes, often requiring thousands of primitives to approximate
    detailed geometry. We introduce Deformable Radial Kernel (DRK), which extends
    Gaussian splatting into a more general and flexible framework. Through learnable
    radial bases with adjustable angles and scales, DRK efficiently models diverse
    shape primitives while enabling precise control over edge sharpness and boundary
    curvature. iven DRK''s planar nature, we further develop accurate ray-primitive
    intersection computation for depth sorting and introduce efficient kernel culling
    strategies for improved rasterization efficiency. Extensive experiments demonstrate
    that DRK outperforms existing methods in both representation efficiency and rendering
    quality, achieving state-of-the-art performance while dramatically reducing primitive
    count.

    '
  project_page: https://yihua7.github.io/DRK-web/
  paper: https://arxiv.org/pdf/2412.11752v1.pdf
  code: null
  video: null
  tags:
  - Optimization
  - Project
  - Rendering
  thumbnail: assets/thumbnails/huang2024deformable.jpg
  publication_date: '2024-12-16T13:11:02+00:00'
  date_source: arxiv
- id: xu2024gaussianproperty
  title: 'GaussianProperty: Integrating Physical Properties to 3D Gaussians with LMMs'
  authors: Xinli Xu, Wenhang Ge, Dicong Qiu, ZhiFei Chen, Dongyu Yan, Zhuoyun Liu,
    Haoyu Zhao, Hanfeng Zhao, Shunsi Zhang, Junwei Liang, Ying-Cong Chen
  year: '2024'
  abstract: 'Estimating physical properties for visual data is a crucial task in computer
    vision, graphics, and robotics, underpinning applications such as augmented reality,
    physical simulation, and robotic grasping. However, this area remains under-explored
    due to the inherent ambiguities in physical property estimation. To address these
    challenges, we introduce GaussianProperty, a training-free framework that assigns
    physical properties of materials to 3D Gaussians. Specifically, we integrate the
    segmentation capability of SAM with the recognition capability of GPT-4V(ision)
    to formulate a global-local physical property reasoning module for 2D images.
    Then we project the physical properties from multi-view 2D images to 3D Gaussians
    using a voting strategy. We demonstrate that 3D Gaussians with physical property
    annotations enable applications in physics-based dynamic simulation and robotic
    grasping. For physics-based dynamic simulation, we leverage the Material Point
    Method (MPM) for realistic dynamic simulation. For robot grasping, we develop
    a grasping force prediction strategy that estimates a safe force range required
    for object grasping based on the estimated physical properties. Extensive experiments
    on material segmentation, physics-based dynamic simulation, and robotic grasping
    validate the effectiveness of our proposed method, highlighting its crucial role
    in understanding physical properties from visual data. Online demo, code, more
    cases and annotated datasets are available on \href{https://Gaussian-Property.github.io}{this
    https URL}.

    '
  project_page: https://gaussian-property.github.io/
  paper: https://arxiv.org/pdf/2412.11258.pdf
  code: https://github.com/xxlbigbrother/Gaussian-Property
  video: null
  tags:
  - Code
  - Language Embedding
  - Project
  - Robotics
  thumbnail: assets/thumbnails/xu2024gaussianproperty.jpg
  publication_date: '2024-12-15T17:44:10+00:00'
  date_source: arxiv
- id: liang2024supergseg
  title: 'SuperGSeg: Open-Vocabulary 3D Segmentation with Structured Super-Gaussians'
  authors: Siyun Liang, Sen Wang, Kunyi Li, Michael Niemeyer, Stefano Gasperini, Nassir
    Navab, Federico Tombari
  year: '2024'
  abstract: '3D Gaussian Splatting has recently gained traction for its efficient
    training and real-time rendering. While the vanilla Gaussian Splatting representation
    is mainly designed for view synthesis, more recent works investigated how to extend
    it with scene understanding and language features. However, existing methods lack
    a detailed comprehension of scenes, limiting their ability to segment and interpret
    complex structures. To this end, We introduce SuperGSeg, a novel approach that
    fosters cohesive, context-aware scene representation by disentangling segmentation
    and language field distillation. SuperGSeg first employs neural Gaussians to learn
    instance and hierarchical segmentation features from multi-view images with the
    aid of off-the-shelf 2D masks. These features are then leveraged to create a sparse
    set of what we call Super-Gaussians. Super-Gaussians facilitate the distillation
    of 2D language features into 3D space. Through Super-Gaussians, our method enables
    high-dimensional language feature rendering without extreme increases in GPU memory.
    Extensive experiments demonstrate that SuperGSeg outperforms prior works on both
    open-vocabulary object localization and semantic segmentation tasks.

    '
  project_page: https://supergseg.github.io/
  paper: https://arxiv.org/pdf/2412.10231.pdf
  code: null
  video: null
  tags:
  - Language Embedding
  - Project
  - Segmentation
  thumbnail: assets/thumbnails/liang2024supergseg.jpg
  publication_date: '2024-12-13T16:01:19+00:00'
  date_source: arxiv
- id: tang2024gaf
  title: 'GAF: Gaussian Avatar Reconstruction from Monocular Videos via Multi-view
    Diffusion'
  authors: Jiapeng Tang, Davide Davoli, Tobias Kirschstein, Liam Schoneveld, Matthias
    Nießner
  year: '2024'
  abstract: We propose a novel approach for reconstructing animatable 3D Gaussian
    avatars from monocular videos captured by commodity devices like smartphones.
    Photorealistic 3D head avatar reconstruction from such recordings is challenging
    due to limited observations, which leaves unobserved regions under-constrained
    and can lead to artifacts in novel views. To address this problem, we introduce
    a multi-view head diffusion model, leveraging its priors to fill in missing regions
    and ensure view consistency in Gaussian splatting renderings. To enable precise
    viewpoint control, we use normal maps rendered from FLAME-based head reconstruction,
    which provides pixel-aligned inductive biases. We also condition the diffusion
    model on VAE features extracted from the input image to preserve details of facial
    identity and appearance. For Gaussian avatar reconstruction, we distill multi-view
    diffusion priors by using iteratively denoised images as pseudo-ground truths,
    effectively mitigating over-saturation issues. To further improve photorealism,
    we apply latent upsampling to refine the denoised latent before decoding it into
    an image. We evaluate our method on the NeRSemble dataset, showing that GAF outperforms
    the previous state-of-the-art methods in novel view synthesis and novel expression
    animation. Furthermore, we demonstrate higher-fidelity avatar reconstructions
    from monocular videos captured on commodity devices.
  project_page: https://tangjiapeng.github.io/projects/GAF/
  paper: https://arxiv.org/pdf/2412.10209
  code: null
  video: https://www.youtube.com/embed/QuIYTljvhygE
  tags:
  - Avatar
  - Project
  - Video
  thumbnail: assets/thumbnails/tang2024gaf.jpg
  publication_date: '2024-12-13T15:31:22+00:00'
- id: park2024splinegs
  title: 'SplineGS: Robust Motion-Adaptive Spline for Real-Time Dynamic 3D Gaussians
    from Monocular Video'
  authors: Jongmin Park, Minh-Quan Viet Bui, Juan Luis Gonzalez Bello, Jaeho Moon,
    Jihyong Oh, Munchurl Kim
  year: '2024'
  abstract: 'Synthesizing novel views from in-the-wild monocular videos is challenging
    due to scene dynamics and the lack of multi-view cues. To address this, we propose
    SplineGS, a COLMAP-free dynamic 3D Gaussian Splatting (3DGS) framework for high-quality
    reconstruction and fast rendering from monocular videos. At its core is a novel
    Motion-Adaptive Spline (MAS) method, which represents continuous dynamic 3D Gaussian
    trajectories using cubic Hermite splines with a small number of control points.
    For MAS, we introduce a Motion-Adaptive Control points Pruning (MACP) method to
    model the deformation of each dynamic 3D Gaussian across varying motions, progressively
    pruning control points while maintaining dynamic modeling integrity. Additionally,
    we present a joint optimization strategy for camera parameter estimation and 3D
    Gaussian attributes, leveraging photometric and geometric consistency. This eliminates
    the need for Structure-from-Motion preprocessing and enhances SplineGS''s robustness
    in real-world conditions. Experiments show that SplineGS significantly outperforms
    state-of-the-art methods in novel view synthesis quality for dynamic scenes from
    monocular videos, achieving thousands times faster rendering speed.

    '
  project_page: https://kaist-viclab.github.io/splinegs-site/
  paper: https://arxiv.org/pdf/2412.09982v1.pdf
  code: null
  video: null
  tags:
  - Dynamic
  - Monocular
  - Project
  thumbnail: assets/thumbnails/park2024splinegs.jpg
  publication_date: '2024-12-13T09:09:14+00:00'
  date_source: arxiv
- id: xu2024representing
  title: Representing Long Volumetric Video with Temporal Gaussian Hierarchy
  authors: Zhen Xu, Yinghao Xu, Zhiyuan Yu, Sida Peng, Jiaming Sun, Hujun Bao, Xiaowei
    Zhou
  year: '2024'
  abstract: 'This paper aims to address the challenge of reconstructing long volumetric
    videos from multi-view RGB videos. Recent dynamic view synthesis methods leverage
    powerful 4D representations, like feature grids or point cloud sequences, to achieve
    high-quality rendering results. However, they are typically limited to short (1~2s)
    video clips and often suffer from large memory footprints when dealing with longer
    videos. To solve this issue, we propose a novel 4D representation, named Temporal
    Gaussian Hierarchy, to compactly model long volumetric videos. Our key observation
    is that there are generally various degrees of temporal redundancy in dynamic
    scenes, which consist of areas changing at different speeds. Motivated by this,
    our approach builds a multi-level hierarchy of 4D Gaussian primitives, where each
    level separately describes scene regions with different degrees of content change,
    and adaptively shares Gaussian primitives to represent unchanged scene content
    over different temporal segments, thus effectively reducing the number of Gaussian
    primitives. In addition, the tree-like structure of the Gaussian hierarchy allows
    us to efficiently represent the scene at a particular moment with a subset of
    Gaussian primitives, leading to nearly constant GPU memory usage during the training
    or rendering regardless of the video length. Extensive experimental results demonstrate
    the superiority of our method over alternative methods in terms of training cost,
    rendering speed, and storage usage. To our knowledge, this work is the first approach
    capable of efficiently handling minutes of volumetric video data while maintaining
    state-of-the-art rendering quality. Our project page is available at: https://zju3dv.github.io/longvolcap.

    '
  project_page: https://zju3dv.github.io/longvolcap/
  paper: https://arxiv.org/pdf/2412.09608.pdf
  code: null
  video: https://www.youtube.com/watch?v=y7e0YRNNmXw&feature=youtu.be
  tags:
  - Acceleration
  - Dynamic
  - Project
  - Video
  thumbnail: assets/thumbnails/xu2024representing.jpg
  publication_date: '2024-12-12T18:59:34+00:00'
- id: chen2024feat2gs
  title: 'Feat2GS: Probing Visual Foundation Models with Gaussian Splatting'
  authors: Yue Chen, Xingyu Chen, Anpei Chen, Gerard Pons-Moll, Yuliang Xiu
  year: '2024'
  abstract: 'Given that visual foundation models (VFMs) are trained on extensive datasets
    but often limited to 2D images, a natural question arises: how well do they understand
    the 3D world? With the differences in architecture and training protocols (i.e.,
    objectives, proxy tasks), a unified framework to fairly and comprehensively probe
    their 3D awareness is urgently needed. Existing works on 3D probing suggest single-view
    2.5D estimation (e.g., depth and normal) or two-view sparse 2D correspondence
    (e.g., matching and tracking). Unfortunately, these tasks ignore texture awareness,
    and require 3D data as ground-truth, which limits the scale and diversity of their
    evaluation set. To address these issues, we introduce Feat2GS, which readout 3D
    Gaussians attributes from VFM features extracted from unposed images. This allows
    us to probe 3D awareness for geometry and texture via novel view synthesis, without
    requiring 3D data. Additionally, the disentanglement of 3DGS parameters - geometry
    ($\boldsymbol{x}, \alpha, \Sigma$) and texture ($\boldsymbol{c}$) - enables separate
    analysis of texture and geometry awareness. Under Feat2GS, we conduct extensive
    experiments to probe the 3D awareness of several VFMs, and investigate the ingredients
    that lead to a 3D aware VFM. Building on these findings, we develop several variants
    that achieve state-of-the-art across diverse datasets. This makes Feat2GS useful
    for probing VFMs, and as a simple-yet-effective baseline for novel-view synthesis.

    '
  project_page: https://fanegg.github.io/Feat2GS/
  paper: https://arxiv.org/pdf/2412.09606.pdf
  code: null
  video: https://youtu.be/4fT5lzcAJqo
  tags:
  - Project
  - Rendering
  - Video
  - World Generation
  thumbnail: assets/thumbnails/chen2024feat2gs.jpg
  publication_date: '2024-12-12T18:59:28+00:00'
  date_source: arxiv
- id: li2024simavatar
  title: 'SimAvatar: Simulation-Ready Avatars with Layered Hair and Clothing'
  authors: Xueting Li, Ye Yuan, Shalini De Mello, Gilles Daviet, Jonathan Leaf, Miles
    Macklin, Jan Kautz, Umar Iqbal
  year: '2024'
  abstract: 'We introduce SimAvatar, a framework designed to generate simulation-ready
    clothed 3D human avatars from a text prompt. Current text-driven human avatar
    generation methods either model hair, clothing, and the human body using a unified
    geometry or produce hair and garments that are not easily adaptable for simulation
    within existing simulation pipelines. The primary challenge lies in representing
    the hair and garment geometry in a way that allows leveraging established prior
    knowledge from foundational image diffusion models (e.g., Stable Diffusion) while
    being simulation-ready using either physics or neural simulators. To address this
    task, we propose a two-stage framework that combines the flexibility of 3D Gaussians
    with simulation-ready hair strands and garment meshes. Specifically, we first
    employ three text-conditioned 3D generative models to generate garment mesh, body
    shape and hair strands from the given text prompt. To leverage prior knowledge
    from foundational diffusion models, we attach 3D Gaussians to the body mesh, garment
    mesh, as well as hair strands and learn the avatar appearance through optimization.
    To drive the avatar given a pose sequence, we first apply physics simulators onto
    the garment meshes and hair strands. We then transfer the motion onto 3D Gaussians
    through carefully designed mechanisms for each body part. As a result, our synthesized
    avatars have vivid texture and realistic dynamic motion. To the best of our knowledge,
    our method is the first to produce highly realistic, fully simulation-ready 3D
    avatars, surpassing the capabilities of current approaches.

    '
  project_page: https://nvlabs.github.io/SimAvatar/
  paper: https://arxiv.org/pdf/2412.09545.pdf
  code: null
  video: https://www.youtube.com/watch?v=qEwBY7LBW2Y
  tags:
  - Avatar
  - Diffusion
  - Language Embedding
  - Project
  - Video
  thumbnail: assets/thumbnails/li2024simavatar.jpg
  publication_date: '2024-12-12T18:35:26+00:00'
  date_source: arxiv
- id: liu2024slam3r
  title: 'SLAM3R: Real-Time Dense Scene Reconstruction from Monocular RGB Videos'
  authors: Yuzheng Liu, Siyan Dong, Shuzhe Wang, Yanchao Yang, Qingnan Fan, Baoquan
    Chen
  year: '2024'
  abstract: 'In this paper, we introduce SLAM3R, a novel and effective monocular RGB
    SLAM system for real-time and high-quality dense 3D reconstruction. SLAM3R provides
    an end-to-end solution by seamlessly integrating local 3D reconstruction and global
    coordinate registration through feed-forward neural networks. Given an input video,
    the system first converts it into overlapping clips using a sliding window mechanism.
    Unlike traditional pose optimization-based methods, SLAM3R directly regresses
    3D pointmaps from RGB images in each window and progressively aligns and deforms
    these local pointmaps to create a globally consistent scene reconstruction - all
    without explicitly solving any camera parameters. Experiments across datasets
    consistently show that SLAM3R achieves state-of-the-art reconstruction accuracy
    and completeness while maintaining real-time performance at 20+ FPS. Code and
    weights at: https://github.com/PKU-VCL-3DV/SLAM3R.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2412.09401.pdf
  code: https://github.com/PKU-VCL-3DV/SLAM3R
  video: null
  tags:
  - 3ster-based
  - Code
  - SLAM
  thumbnail: assets/thumbnails/liu2024slam3r.jpg
  publication_date: '2024-12-12T16:08:03+00:00'
  date_source: arxiv
- id: gomel2024diffusionbased
  title: Diffusion-Based Attention Warping for Consistent 3D Scene Editing
  authors: Eyal Gomel, Lior Wolf
  year: '2024'
  abstract: 'We present a novel method for 3D scene editing using diffusion models,
    designed to ensure view consistency and realism across perspectives. Our approach
    leverages attention features extracted from a single reference image to define
    the intended edits. These features are warped across multiple views by aligning
    them with scene geometry derived from Gaussian splatting depth estimates. Injecting
    these warped features into other viewpoints enables coherent propagation of edits,
    achieving high fidelity and spatial alignment in 3D space. Extensive evaluations
    demonstrate the effectiveness of our method in generating versatile edits of 3D
    scenes, significantly advancing the capabilities of scene manipulation compared
    to the existing methods. Project page: \url{https://attention-warp.github.io}

    '
  project_page: https://attention-warp.github.io/
  paper: https://arxiv.org/pdf/2412.07984.pdf
  code: null
  video: null
  tags:
  - Diffusion
  - Project
  - Style Transfer
  thumbnail: assets/thumbnails/gomel2024diffusionbased.jpg
  publication_date: '2024-12-10T23:57:18+00:00'
  date_source: arxiv
- id: saunders2024gasp
  title: 'GASP: Gaussian Avatars with Synthetic Priors'
  authors: Jack Saunders, Charlie Hewitt, Yanan Jian, Marek Kowalski, Tadas Baltrušaitis,
    Yiye Chen, Darren Cosker, Virginia Estellers, Nicholas Gyde, Vinay Namboodiri,
    Benjamin Lundell
  year: '2024'
  abstract: 'Gaussian Splatting has changed the game for real-time photo-realistic
    rendering. One of the most popular applications of Gaussian Splatting is to create
    animatable avatars, known as Gaussian Avatars. Recent works have pushed the boundaries
    of quality and rendering efficiency but suffer from two main limitations. Either
    they require expensive multi-camera rigs to produce avatars with free-view rendering,
    or they can be trained with a single camera but only rendered at high quality
    from this fixed viewpoint. An ideal model would be trained using a short monocular
    video or image from available hardware, such as a webcam, and rendered from any
    view. To this end, we propose GASP: Gaussian Avatars with Synthetic Priors. To
    overcome the limitations of existing datasets, we exploit the pixel-perfect nature
    of synthetic data to train a Gaussian Avatar prior. By fitting this prior model
    to a single photo or video and fine-tuning it, we get a high-quality Gaussian
    Avatar, which supports 360-degree rendering. Our prior is only required for fitting,
    not inference, enabling real-time application. Through our method, we obtain high-quality,
    animatable Avatars from limited data which can be animated and rendered at 70fps
    on commercial hardware.'
  project_page: https://microsoft.github.io/GASP/
  paper: https://arxiv.org/pdf/2412.07739
  code: null
  video: https://www.youtube.com/watch?v=3oWB7-UJUYE
  tags:
  - Avatar
  - Project
  - Video
  thumbnail: assets/thumbnails/saunders2024gasp.jpg
  publication_date: '2024-12-10T18:36:21+00:00'
- id: wang2024faster
  title: Faster and Better 3D Splatting via Group Training
  authors: Chengbo Wang, Guozheng Ma, Yifei Xue, Yizhen Lao
  year: '2024'
  abstract: '3D Gaussian Splatting (3DGS) has emerged as a powerful technique for
    novel view synthesis, demonstrating remarkable capability in high-fidelity scene
    reconstruction through its Gaussian primitive representations. However, the computational
    overhead induced by the massive number of primitives poses a significant bottleneck
    to training efficiency. To overcome this challenge, we propose Group Training,
    a simple yet effective strategy that organizes Gaussian primitives into manageable
    groups, optimizing training efficiency and improving rendering quality. This approach
    shows universal compatibility with existing 3DGS frameworks, including vanilla
    3DGS and Mip-Splatting, consistently achieving accelerated training while maintaining
    superior synthesis quality. Extensive experiments reveal that our straightforward
    Group Training strategy achieves up to 30% faster convergence and improved rendering
    quality across diverse scenarios.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2412.07608.pdf
  code: null
  video: null
  tags:
  - Acceleration
  - Densification
  - Optimization
  thumbnail: assets/thumbnails/wang2024faster.jpg
  publication_date: '2024-12-10T15:47:17+00:00'
  date_source: arxiv
- id: li2024recap
  title: 'ReCap: Better Gaussian Relighting with Cross-Environment Captures'
  authors: Jingzhi Li, Zongwei Wu, Eduard Zamfir, Radu Timofte
  year: '2024'
  abstract: 'Accurate 3D objects relighting in diverse unseen environments is crucial
    for realistic virtual object placement. Due to the albedo-lighting ambiguity,
    existing methods often fall short in producing faithful relights. Without proper
    constraints, observed training views can be explained by numerous combinations
    of lighting and material attributes, lacking physical correspondence with the
    actual environment maps used for relighting. In this work, we present ReCap, treating
    cross-environment captures as multi-task target to provide the missing supervision
    that cuts through the entanglement. Specifically, ReCap jointly optimizes multiple
    lighting representations that share a common set of material attributes. This
    naturally harmonizes a coherent set of lighting representations around the mutual
    material attributes, exploiting commonalities and differences across varied object
    appearances. Such coherence enables physically sound lighting reconstruction and
    robust material estimation - both essential for accurate relighting. Together
    with a streamlined shading function and effective post-processing, ReCap outperforms
    the leading competitor by 3.4 dB in PSNR on an expanded relighting benchmark.

    '
  project_page: https://jingzhi.github.io/ReCap/
  paper: https://arxiv.org/pdf/2412.07534.pdf
  code: null
  video: null
  tags:
  - Project
  - Relight
  thumbnail: assets/thumbnails/li2024recap.jpg
  publication_date: '2024-12-10T14:15:32+00:00'
  date_source: arxiv
- id: lyu2024resgs
  title: 'ResGS: Residual Densification of 3D Gaussian for Efficient Detail Recovery'
  authors: Yanzhe Lyu, Kai Cheng, Xin Kang, Xuejin Chen
  year: '2024'
  abstract: 'Recently, 3D Gaussian Splatting (3D-GS) has prevailed in novel view synthesis,
    achieving high fidelity and efficiency. However, it often struggles to capture
    rich details and complete geometry. Our analysis highlights a key limitation of
    3D-GS caused by the fixed threshold in densification, which balances geometry
    coverage against detail recovery as the threshold varies. To address this, we
    introduce a novel densification method, residual split, which adds a downscaled
    Gaussian as a residual. Our approach is capable of adaptively retrieving details
    and complementing missing geometry while enabling progressive refinement. To further
    support this method, we propose a pipeline named ResGS. Specifically, we integrate
    a Gaussian image pyramid for progressive supervision and implement a selection
    scheme that prioritizes the densification of coarse Gaussians over time. Extensive
    experiments demonstrate that our method achieves SOTA rendering quality. Consistent
    performance improvements can be achieved by applying our residual split on various
    3D-GS variants, underscoring its versatility and potential for broader application
    in 3D-GS-based applications.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2412.07494.pdf
  code: null
  video: null
  tags:
  - Densification
  thumbnail: assets/thumbnails/lyu2024resgs.jpg
  publication_date: '2024-12-10T13:19:27+00:00'
  date_source: arxiv
- id: tang2024mvdust3r
  title: 'MV-DUSt3R+: Single-Stage Scene Reconstruction from Sparse Views In 2 Seconds'
  authors: Zhenggang Tang, Yuchen Fan, Dilin Wang, Hongyu Xu, Rakesh Ranjan, Alexander
    Schwing, Zhicheng Yan
  year: '2024'
  abstract: 'Recent sparse multi-view scene reconstruction advances like DUSt3R and
    MASt3R no longer require camera calibration and camera pose estimation. However,
    they only process a pair of views at a time to infer pixel-aligned pointmaps.
    When dealing with more than two views, a combinatorial number of error prone pairwise
    reconstructions are usually followed by an expensive global optimization, which
    often fails to rectify the pairwise reconstruction errors. To handle more views,
    reduce errors, and improve inference time, we propose the fast single-stage feed-forward
    network MV-DUSt3R. At its core are multi-view decoder blocks which exchange information
    across any number of views while considering one reference view. To make our method
    robust to reference view selection, we further propose MV-DUSt3R+, which employs
    cross-reference-view blocks to fuse information across different reference view
    choices. To further enable novel view synthesis, we extend both by adding and
    jointly training Gaussian splatting heads. Experiments on multi-view stereo reconstruction,
    multi-view pose estimation, and novel view synthesis confirm that our methods
    improve significantly upon prior art. Code will be released.

    '
  project_page: https://mv-dust3rp.github.io/
  paper: https://arxiv.org/pdf/2412.06974.pdf
  code: https://github.com/facebookresearch/mvdust3r
  video: https://youtu.be/LBvnuKQ8Rso
  tags:
  - 3ster-based
  - Code
  - Project
  - Sparse
  - Video
  thumbnail: assets/thumbnails/tang2024mvdust3r.jpg
  publication_date: '2024-12-09T20:34:55+00:00'
  date_source: arxiv
- id: guédon2024matcha
  title: 'MAtCha Gaussians: Atlas of Charts for High-Quality Geometry and Photorealism
    From Sparse Views'
  authors: Antoine Guédon, Tomoki Ichikawa, Kohei Yamashita, Ko Nishino
  year: '2024'
  abstract: 'We present a novel appearance model that simultaneously realizes explicit
    high-quality 3D surface mesh recovery and photorealistic novel view synthesis
    from sparse view samples. Our key idea is to model the underlying scene geometry
    Mesh as an Atlas of Charts which we render with 2D Gaussian surfels (MAtCha Gaussians).
    MAtCha distills high-frequency scene surface details from an off-the-shelf monocular
    depth estimator and refines it through Gaussian surfel rendering. The Gaussian
    surfels are attached to the charts on the fly, satisfying photorealism of neural
    volumetric rendering and crisp geometry of a mesh model, i.e., two seemingly contradicting
    goals in a single model. At the core of MAtCha lies a novel neural deformation
    model and a structure loss that preserve the fine surface details distilled from
    learned monocular depths while addressing their fundamental scale ambiguities.
    Results of extensive experimental validation demonstrate MAtCha''s state-of-the-art
    quality of surface reconstruction and photorealism on-par with top contenders
    but with dramatic reduction in the number of input views and computational time.
    We believe MAtCha will serve as a foundational tool for any visual application
    in vision, graphics, and robotics that require explicit geometry in addition to
    photorealism. Our project page is the following: https://anttwo.github.io/matcha/

    '
  project_page: https://anttwo.github.io/matcha/
  paper: https://arxiv.org/pdf/2412.06767.pdf
  code: null
  video: null
  tags:
  - Meshing
  - Project
  - Sparse
  thumbnail: assets/thumbnails/guédon2024matcha.jpg
  publication_date: '2024-12-09T18:55:23+00:00'
  date_source: arxiv
- id: wu2024deblur4dgs
  title: 'Deblur4DGS: 4D Gaussian Splatting from Blurry Monocular Video'
  authors: Renlong Wu, Zhilu Zhang, Mingyang Chen, Xiaopeng Fan, Zifei Yan, Wangmeng
    Zuo
  year: '2024'
  abstract: 'Recent 4D reconstruction methods have yielded impressive results but
    rely on sharp videos as supervision. However, motion blur often occurs in videos
    due to camera shake and object movement, while existing methods render blurry
    results when using such videos for reconstructing 4D models. Although a few NeRF-based
    approaches attempted to address the problem, they struggled to produce high-quality
    results, due to the inaccuracy in estimating continuous dynamic representations
    within the exposure time. Encouraged by recent works in 3D motion trajectory modeling
    using 3D Gaussian Splatting (3DGS), we suggest taking 3DGS as the scene representation
    manner, and propose the first 4D Gaussian Splatting framework to reconstruct a
    high-quality 4D model from blurry monocular video, named Deblur4DGS. Specifically,
    we transform continuous dynamic representations estimation within an exposure
    time into the exposure time estimation. Moreover, we introduce exposure regularization
    to avoid trivial solutions, as well as multi-frame and multi-resolution consistency
    ones to alleviate artifacts. Furthermore, to better represent objects with large
    motion, we suggest blur-aware variable canonical Gaussians. Beyond novel-view
    synthesis, Deblur4DGS can be applied to improve blurry video from multiple perspectives,
    including deblurring, frame interpolation, and video stabilization. Extensive
    experiments on the above four tasks show that Deblur4DGS outperforms state-of-the-art
    4D reconstruction methods. The codes are available at https://github.com/ZcsrenlongZ/Deblur4DGS.

    '
  project_page: https://deblur4dgs.github.io/
  paper: https://arxiv.org/pdf/2412.06424.pdf
  code: https://github.com/ZcsrenlongZ/Deblur4DGS
  video: https://www.youtube.com/watch?v=1I2N2IDneUQ
  tags:
  - Code
  - Deblurring
  - Project
  - Video
  thumbnail: assets/thumbnails/wu2024deblur4dgs.jpg
  publication_date: '2024-12-09T12:02:11+00:00'
  date_source: arxiv
- id: yan20244d
  title: 4D Gaussian Splatting with Scale-aware Residual Field and Adaptive Optimization
    for Real-time Rendering of Temporally Complex Dynamic Scenes
  authors: Jinbo Yan, Rui Peng, Luyang Tang, Ronggang Wang
  year: '2024'
  abstract: 'Reconstructing dynamic scenes from video sequences is a highly promising
    task in the multimedia domain. While previous methods have made progress, they
    often struggle with slow rendering and managing temporal complexities such as
    significant motion and object appearance/disappearance. In this paper, we propose
    SaRO-GS as a novel dynamic scene representation capable of achieving real-time
    rendering while effectively handling temporal complexities in dynamic scenes.
    To address the issue of slow rendering speed, we adopt a Gaussian primitive-based
    representation and optimize the Gaussians in 4D space, which facilitates real-time
    rendering with the assistance of 3D Gaussian Splatting. Additionally, to handle
    temporally complex dynamic scenes, we introduce a Scale-aware Residual Field.
    This field considers the size information of each Gaussian primitive while encoding
    its residual feature and aligns with the self-splitting behavior of Gaussian primitives.
    Furthermore, we propose an Adaptive Optimization Schedule, which assigns different
    optimization strategies to Gaussian primitives based on their distinct temporal
    properties, thereby expediting the reconstruction of dynamic regions. Through
    evaluations on monocular and multi-view datasets, our method has demonstrated
    state-of-the-art performance. Please see our project page at https://yjb6.github.io/SaRO-GS.github.io.

    '
  project_page: https://yjb6.github.io/SaRO-GS.github.io/
  paper: https://arxiv.org/pdf/2412.06299.pdf
  code: https://github.com/yjb6/SaRO-GS
  video: null
  tags:
  - Code
  - Densification
  - Dynamic
  - Optimization
  - Project
  thumbnail: assets/thumbnails/yan20244d.jpg
  publication_date: '2024-12-09T08:44:19+00:00'
  date_source: arxiv
- id: qiu2024advancing
  title: 'Advancing Extended Reality with 3D Gaussian Splatting: Innovations and Prospects'
  authors: Shi Qiu, Binzhu Xie, Qixuan Liu, Pheng-Ann Heng
  year: '2024'
  abstract: 3D Gaussian Splatting (3DGS) has attracted significant attention for its
    potential to revolutionize 3D representation, rendering, and interaction. Despite
    the rapid growth of 3DGS research, its direct application to Extended Reality
    (XR) remains underexplored. Although many studies recognize the potential of 3DGS
    for XR, few have explicitly focused on or demonstrated its effectiveness within
    XR environments. In this paper, we aim to synthesize innovations in 3DGS that
    show specific potential for advancing XR research and development. We conduct
    a comprehensive review of publicly available 3DGS papers, with a focus on those
    referencing XR-related concepts. Additionally, we perform an in-depth analysis
    of innovations explicitly relevant to XR and propose a taxonomy to highlight their
    significance. Building on these insights, we propose several prospective XR research
    areas where 3DGS can make promising contributions, yet remain rarely touched.
    By investigating the intersection of 3DGS and XR, this paper provides a roadmap
    to push the boundaries of XR using cutting-edge 3DGS techniques.
  project_page: null
  paper: https://arxiv.org/pdf/2412.06257
  code: null
  video: null
  tags:
  - Review
  thumbnail: assets/thumbnails/qiu2024advancing.jpg
  publication_date: '2024-12-09T07:14:58+00:00'
- id: javed2024temporally
  title: Temporally Compressed 3D Gaussian Splatting for Dynamic Scenes
  authors: Saqib Javed, Ahmad Jarrar Khan, Corentin Dumery, Chen Zhao, Mathieu Salzmann
  year: '2024'
  abstract: 'Recent advancements in high-fidelity dynamic scene reconstruction have
    leveraged dynamic 3D Gaussians and 4D Gaussian Splatting for realistic scene representation.
    However, to make these methods viable for real-time applications such as AR/VR,
    gaming, and rendering on low-power devices, substantial reductions in memory usage
    and improvements in rendering efficiency are required. While many state-of-the-art
    methods prioritize lightweight implementations, they struggle in handling scenes
    with complex motions or long sequences. In this work, we introduce Temporally
    Compressed 3D Gaussian Splatting (TC3DGS), a novel technique designed specifically
    to effectively compress dynamic 3D Gaussian representations. TC3DGS selectively
    prunes Gaussians based on their temporal relevance and employs gradient-aware
    mixed-precision quantization to dynamically compress Gaussian parameters. It additionally
    relies on a variation of the Ramer-Douglas-Peucker algorithm in a post-processing
    step to further reduce storage by interpolating Gaussian trajectories across frames.
    Our experiments across multiple datasets demonstrate that TC3DGS achieves up to
    67$\times$ compression with minimal or no degradation in visual quality.

    '
  project_page: https://ahmad-jarrar.github.io/tc-3dgs/
  paper: https://arxiv.org/pdf/2412.05700.pdf
  code: null
  video: null
  tags:
  - Acceleration
  - Compression
  - Dynamic
  - Project
  thumbnail: assets/thumbnails/javed2024temporally.jpg
  publication_date: '2024-12-07T17:03:09+00:00'
  date_source: arxiv
- id: fan2024momentumgs
  title: 'Momentum-GS: Momentum Gaussian Self-Distillation for High-Quality Large
    Scene Reconstruction'
  authors: Jixuan Fan, Wanhua Li, Yifei Han, Yansong Tang
  year: '2024'
  abstract: '3D Gaussian Splatting has demonstrated notable success in large-scale
    scene reconstruction, but challenges persist due to high training memory consumption
    and storage overhead. Hybrid representations that integrate implicit and explicit
    features offer a way to mitigate these limitations. However, when applied in parallelized
    block-wise training, two critical issues arise since reconstruction accuracy deteriorates
    due to reduced data diversity when training each block independently, and parallel
    training restricts the number of divided blocks to the available number of GPUs.
    To address these issues, we propose Momentum-GS, a novel approach that leverages
    momentum-based self-distillation to promote consistency and accuracy across the
    blocks while decoupling the number of blocks from the physical GPU count. Our
    method maintains a teacher Gaussian decoder updated with momentum, ensuring a
    stable reference during training. This teacher provides each block with global
    guidance in a self-distillation manner, promoting spatial consistency in reconstruction.
    To further ensure consistency across the blocks, we incorporate block weighting,
    dynamically adjusting each block''s weight according to its reconstruction accuracy.
    Extensive experiments on large-scale scenes show that our method consistently
    outperforms existing techniques, achieving a 12.8% improvement in LPIPS over CityGaussian
    with much fewer divided blocks and establishing a new state of the art. Project
    page: https://jixuan-fan.github.io/Momentum-GS_Page/

    '
  project_page: https://jixuan-fan.github.io/Momentum-GS_Page/
  paper: https://arxiv.org/pdf/2412.04887.pdf
  code: https://github.com/Jixuan-Fan/Momentum-GS
  video: https://jixuan-fan.github.io/Momentum-GS_Page/static/videos/Momentum-GS_demo.mp4
  tags:
  - Code
  - Large-Scale
  - Project
  - Video
  thumbnail: assets/thumbnails/fan2024momentumgs.jpg
  publication_date: '2024-12-06T09:31:12+00:00'
  date_source: arxiv
- id: liang2024feedforward
  title: Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular
    Videos
  authors: Hanxue Liang, Jiawei Ren, Ashkan Mirzaei, Antonio Torralba, Ziwei Liu,
    Igor Gilitschenski, Sanja Fidler, Cengiz Oztireli, Huan Ling, Zan Gojcic, Jiahui
    Huang
  year: '2024'
  abstract: 'Recent advancements in static feed-forward scene reconstruction have
    demonstrated significant progress in high-quality novel view synthesis. However,
    these models often struggle with generalizability across diverse environments
    and fail to effectively handle dynamic content. We present BTimer (short for BulletTimer),
    the first motion-aware feed-forward model for real-time reconstruction and novel
    view synthesis of dynamic scenes. Our approach reconstructs the full scene in
    a 3D Gaussian Splatting representation at a given target (''bullet'') timestamp
    by aggregating information from all the context frames. Such a formulation allows
    BTimer to gain scalability and generalization by leveraging both static and dynamic
    scene datasets. Given a casual monocular dynamic video, BTimer reconstructs a
    bullet-time scene within 150ms while reaching state-of-the-art performance on
    both static and dynamic scene datasets, even compared with optimization-based
    approaches.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2412.03526.pdf
  code: null
  video: null
  tags:
  - Dynamic
  - Feed-Forward
  - Monocular
  thumbnail: assets/thumbnails/liang2024feedforward.jpg
  publication_date: '2024-12-04T18:15:06+00:00'
  date_source: arxiv
- id: tan2024planarsplatting
  title: 'PlanarSplatting: Accurate Planar Surface Reconstruction in 3 Minutes'
  authors: Bin Tan, Rui Yu, Yujun Shen, Nan Xue
  year: '2024'
  abstract: 'This paper presents PlanarSplatting, an ultra-fast and accurate surface
    reconstruction approach for multiview indoor images. We take the 3D planes as
    the main objective due to their compactness and structural expressiveness in indoor
    scenes, and develop an explicit optimization framework that learns to fit the
    expected surface of indoor scenes by splatting the 3D planes into 2.5D depth and
    normal maps. As our PlanarSplatting operates directly on the 3D plane primitives,
    it eliminates the dependencies on 2D/3D plane detection and plane matching and
    tracking for planar surface reconstruction. Furthermore, the essential merits
    of plane-based representation plus CUDA-based implementation of planar splatting
    functions, PlanarSplatting reconstructs an indoor scene in 3 minutes while having
    significantly better geometric accuracy. Thanks to our ultra-fast reconstruction
    speed, the largest quantitative evaluation on the ScanNet and ScanNet++ datasets
    over hundreds of scenes clearly demonstrated the advantages of our method. We
    believe that our accurate and ultrafast planar surface reconstruction method will
    be applied in the structured data curation for surface reconstruction in the future.
    The code of our CUDA implementation will be publicly available. Project page:
    https://icetttb.github.io/PlanarSplatting/

    '
  project_page: https://icetttb.github.io/PlanarSplatting/
  paper: https://arxiv.org/pdf/2412.03451.pdf
  code: null
  video: null
  tags:
  - Acceleration
  - Project
  - Rendering
  thumbnail: assets/thumbnails/tan2024planarsplatting.jpg
  publication_date: '2024-12-04T16:38:07+00:00'
  date_source: arxiv
- id: schmidt2024nerf
  title: NeRF and Gaussian Splatting SLAM in the Wild
  authors: Fabian Schmidt, Markus Enzweiler, Abhinav Valada
  year: '2024'
  abstract: 'Navigating outdoor environments with visual Simultaneous Localization
    and Mapping (SLAM) systems poses significant challenges due to dynamic scenes,
    lighting variations, and seasonal changes, requiring robust solutions. While traditional
    SLAM methods struggle with adaptability, deep learning-based approaches and emerging
    neural radiance fields as well as Gaussian Splatting-based SLAM methods, offer
    promising alternatives. However, these methods have primarily been evaluated in
    controlled indoor environments with stable conditions, leaving a gap in understanding
    their performance in unstructured and variable outdoor settings. This study addresses
    this gap by evaluating these methods in natural outdoor environments, focusing
    on camera tracking accuracy, robustness to environmental factors, and computational
    efficiency, highlighting distinct trade-offs. Extensive evaluations demonstrate
    that neural SLAM methods achieve superior robustness, particularly under challenging
    conditions such as low light, but at a high computational cost. At the same time,
    traditional methods perform the best across seasons but are highly sensitive to
    variations in lighting conditions. The code of the benchmark is publicly available
    at https://github.com/iis-esslingen/nerf-3dgs-benchmark.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2412.03263.pdf
  code: https://github.com/iis-esslingen/nerf-3dgs-benchmark
  video: null
  tags:
  - Code
  - In the Wild
  - Review
  - SLAM
  thumbnail: assets/thumbnails/schmidt2024nerf.jpg
  publication_date: '2024-12-04T12:11:19+00:00'
  date_source: arxiv
- id: song2024hdgs
  title: 'HDGS: Textured 2D Gaussian Splatting for Enhanced Scene Rendering'
  authors: Yunzhou Song, Heguang Lin, Jiahui Lei, Lingjie Liu, Kostas Daniilidis
  year: '2024'
  abstract: 'Recent advancements in neural rendering, particularly 2D Gaussian Splatting
    (2DGS), have shown promising results for jointly reconstructing fine appearance
    and geometry by leveraging 2D Gaussian surfels. However, current methods face
    significant challenges when rendering at arbitrary viewpoints, such as anti-aliasing
    for down-sampled rendering, and texture detail preservation for high-resolution
    rendering. We proposed a novel method to align the 2D surfels with texture maps
    and augment it with per-ray depth sorting and fisher-based pruning for rendering
    consistency and efficiency. With correct order, per-surfel texture maps significantly
    improve the capabilities to capture fine details. Additionally, to render high-fidelity
    details in varying viewpoints, we designed a frustum-based sampling method to
    mitigate the aliasing artifacts. Experimental results on benchmarks and our custom
    texture-rich dataset demonstrate that our method surpasses existing techniques,
    particularly in detail preservation and anti-aliasing.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2412.01823.pdf
  code: null
  video: null
  tags:
  - 2DGS
  - Antialiasing
  - Meshing
  thumbnail: assets/thumbnails/song2024hdgs.jpg
  publication_date: '2024-12-02T18:59:09+00:00'
  date_source: arxiv
- id: joanna2024occams
  title: 'Occam''s LGS: A Simple Approach for Language Gaussian Splatting'
  authors: Jiahuan (Joanna) Cheng, Jan-Nico Zaech, Luc Van Gool, Danda Pani Paudel
  year: '2024'
  abstract: 'Gaussian Splatting is a widely adopted approach for 3D scene representation
    that offers efficient, high-quality 3D reconstruction and rendering. A major reason
    for the success of 3DGS is its simplicity of representing a scene with a set of
    Gaussians, which makes it easy to interpret and adapt. To enhance scene understanding
    beyond the visual representation, approaches have been developed that extend 3D
    Gaussian Splatting with semantic vision-language features, especially allowing
    for open-set tasks. In this setting, the language features of 3D Gaussian Splatting
    are often aggregated from multiple 2D views. Existing works address this aggregation
    problem using cumbersome techniques that lead to high computational cost and training
    time. In this work, we show that the sophisticated techniques for language-grounded
    3D Gaussian Splatting are simply unnecessary. Instead, we apply Occam''s razor
    to the task at hand and perform weighted multi-view feature aggregation using
    the weights derived from the standard rendering process, followed by a simple
    heuristic-based noisy Gaussian filtration. Doing so offers us state-of-the-art
    results with a speed-up of two orders of magnitude. We showcase our results in
    two commonly used benchmark datasets: LERF and 3D-OVS. Our simple approach allows
    us to perform reasoning directly in the language features, without any compression
    whatsoever. Such modeling in turn offers easy scene manipulation, unlike the existing
    methods -- which we illustrate using an application of object insertion in the
    scene. Furthermore, we provide a thorough discussion regarding the significance
    of our contributions within the context of the current literature.'
  project_page: https://insait-institute.github.io/OccamLGS/
  paper: https://arxiv.org/pdf/2412.01807
  code: null
  video: null
  tags:
  - Acceleration
  - Language Embedding
  - Project
  - Segmentation
  thumbnail: assets/thumbnails/joanna2024occams.jpg
  publication_date: '2024-12-02T18:50:37+00:00'
- id: li2024dynsup
  title: 'DynSUP: Dynamic Gaussian Splatting from An Unposed Image Pair'
  authors: Weihang Li, Weirong Chen, Shenhan Qian, Jiajie Chen, Daniel Cremers, Haoang
    Li
  year: '2024'
  abstract: Recent advances in 3D Gaussian Splatting have shown promising results.
    Existing methods typically assume static scenes and/or multiple images with prior
    poses. Dynamics, sparse views, and unknown poses significantly increase the problem
    complexity due to insufficient geometric constraints. To overcome this challenge,
    we propose a method that can use only two images without prior poses to fit Gaussians
    in dynamic environments. To achieve this, we introduce two technical contributions.
    First, we propose an object-level two-view bundle adjustment. This strategy decomposes
    dynamic scenes into piece-wise rigid components, and jointly estimates the camera
    pose and motions of dynamic objects. Second, we design an SE(3) field-driven Gaussian
    training method. It enables fine-grained motion modeling through learnable per-Gaussian
    transformations. Our method leads to high-fidelity novel view synthesis of dynamic
    scenes while accurately preserving temporal consistency and object motion. Experiments
    on both synthetic and real-world datasets demonstrate that our method significantly
    outperforms state-of-the-art approaches designed for the cases of static environments,
    multiple images, and/or known poses. Our project page is available at https://colin-de.github.io/DynSUP/.
  project_page: https://colin-de.github.io/DynSUP/
  paper: https://arxiv.org/pdf/2412.00851
  code: null
  video: null
  tags:
  - Dynamic
  - Poses
  - Project
  thumbnail: assets/thumbnails/li2024dynsup.jpg
  publication_date: '2024-12-01T15:25:33+00:00'
- id: hanson2024speedysplat
  title: 'Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives'
  authors: Alex Hanson, Allen Tu, Geng Lin, Vasu Singla, Matthias Zwicker, Tom Goldstein
  year: '2024'
  abstract: '3D Gaussian Splatting (3D-GS) is a recent 3D scene reconstruction technique
    that enables real-time rendering of novel views by modeling scenes as parametric
    point clouds of differentiable 3D Gaussians. However, its rendering speed and
    model size still present bottlenecks, especially in resource-constrained settings.
    In this paper, we identify and address two key inefficiencies in 3D-GS, achieving
    substantial improvements in rendering speed, model size, and training time. First,
    we optimize the rendering pipeline to precisely localize Gaussians in the scene,
    boosting rendering speed without altering visual fidelity. Second, we introduce
    a novel pruning technique and integrate it into the training pipeline, significantly
    reducing model size and training time while further raising rendering speed. Our
    Speedy-Splat approach combines these techniques to accelerate average rendering
    speed by a drastic $6.71\times$ across scenes from the Mip-NeRF 360, Tanks & Temples,
    and Deep Blending datasets with $10.6\times$ fewer primitives than 3D-GS.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2412.00578.pdf
  code: null
  video: null
  tags:
  - Acceleration
  - Sparse
  thumbnail: assets/thumbnails/hanson2024speedysplat.jpg
  publication_date: '2024-11-30T20:25:56+00:00'
  date_source: arxiv
- id: pryadilshchikov2024t3dgs
  title: 'T-3DGS: Removing Transient Objects for 3D Scene Reconstruction'
  authors: Vadim Pryadilshchikov, Alexander Markin, Artem Komarichev, Ruslan Rakhimov,
    Peter Wonka, Evgeny Burnaev
  year: '2024'
  abstract: We propose a novel framework to remove transient objects from input videos
    for 3D scene reconstruction using Gaussian Splatting. Our framework consists of
    the following steps. In the first step, we propose an unsupervised training strategy
    for a classification network to distinguish between transient objects and static
    scene parts based on their different training behavior inside the 3D Gaussian
    Splatting reconstruction. In the second step, we improve the boundary quality
    and stability of the detected transients by combining our results from the first
    step with an off-the-shelf segmentation method. We also propose a simple and effective
    strategy to track objects in the input video forward and backward in time. Our
    results show an improvement over the current state of the art in existing sparsely
    captured datasets and significant improvements in a newly proposed densely captured
    (video) dataset.
  project_page: https://transient-3dgs.github.io/
  paper: https://arxiv.org/pdf/2412.00155
  code: https://github.com/Vadim200116/T-3DGS
  video: null
  tags:
  - Code
  - Project
  - Rendering
  thumbnail: assets/thumbnails/pryadilshchikov2024t3dgs.jpg
  publication_date: '2024-11-29T07:45:24+00:00'
- id: wang2024gaussurf
  title: 'GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction'
  authors: Jiepeng Wang, Yuan Liu, Peng Wang, Cheng Lin, Junhui Hou, Xin Li, Taku
    Komura, Wenping Wang
  year: '2024'
  abstract: '3D Gaussian Splatting has achieved impressive performance in novel view
    synthesis with real-time rendering capabilities. However, reconstructing high-quality
    surfaces with fine details using 3D Gaussians remains a challenging task. In this
    work, we introduce GausSurf, a novel approach to high-quality surface reconstruction
    by employing geometry guidance from multi-view consistency in texture-rich areas
    and normal priors in texture-less areas of a scene. We observe that a scene can
    be mainly divided into two primary regions: 1) texture-rich and 2) texture-less
    areas. To enforce multi-view consistency at texture-rich areas, we enhance the
    reconstruction quality by incorporating a traditional patch-match based Multi-View
    Stereo (MVS) approach to guide the geometry optimization in an iterative scheme.
    This scheme allows for mutual reinforcement between the optimization of Gaussians
    and patch-match refinement, which significantly improves the reconstruction results
    and accelerates the training process. Meanwhile, for the texture-less areas, we
    leverage normal priors from a pre-trained normal estimation model to guide optimization.
    Extensive experiments on the DTU and Tanks and Temples datasets demonstrate that
    our method surpasses state-of-the-art methods in terms of reconstruction quality
    and computation time.'
  project_page: https://jiepengwang.github.io/GausSurf/
  paper: https://arxiv.org/pdf/2411.19454
  code: null
  video: null
  tags:
  - Meshing
  - Project
  thumbnail: assets/thumbnails/wang2024gaussurf.jpg
  publication_date: '2024-11-29T03:54:54+00:00'
- id: li2024sadg
  title: 'SADG: Segment Any Dynamic Gaussians Without Object Trackers'
  authors: Yun-Jin Li, Mariia Gladkova, Yan Xia, Daniel Cremers
  year: '2024'
  abstract: Understanding dynamic 3D scenes is fundamental for various applications,
    including extended reality (XR) and autonomous driving. Effectively integrating
    semantic information into 3D reconstruction enables holistic representation that
    opens opportunities for immersive and interactive applications. We introduce SADG,
    Segment Any Dynamic Gaussian Without Object Trackers, a novel approach that combines
    dynamic Gaussian Splatting representation and semantic information without reliance
    on object IDs. In contrast to existing works, we do not rely on supervision based
    on object identities to enable consistent segmentation of dynamic 3D objects.
    To this end, we propose to learn semantically-aware features by leveraging masks
    generated from the Segment Anything Model (SAM) and utilizing our novel contrastive
    learning objective based on hard pixel mining. The learned Gaussian features can
    be effectively clustered without further post-processing. This enables fast computation
    for further object-level editing, such as object removal, composition, and style
    transfer by manipulating the Gaussians in the scene. We further extend several
    dynamic novel-view datasets with segmentation benchmarks to enable testing of
    learned feature fields from unseen viewpoints. We evaluate SADG on proposed benchmarks
    and demonstrate the superior performance of our approach in segmenting objects
    within dynamic scenes along with its effectiveness for further downstream editing
    tasks.
  project_page: https://yunjinli.github.io/project-sadg/
  paper: https://arxiv.org/pdf/2411.19290
  code: null
  video: https://yunjinli.github.io/project-sadg/static/videos/split-cookie-2x4x.mp4
  tags:
  - Dynamic
  - Project
  - Segmentation
  - Video
  thumbnail: assets/thumbnails/li2024sadg.jpg
  publication_date: '2024-11-28T17:47:48+00:00'
- id: ren2024agsmesh
  title: 'AGS-Mesh: Adaptive Gaussian Splatting and Meshing with Geometric Priors
    for Indoor Room Reconstruction Using Smartphones'
  authors: Xuqian Ren, Matias Turkulainen, Jiepeng Wang, Otto Seiskari, Iaroslav Melekhov,
    Juho Kannala, Esa Rahtu
  year: '2024'
  abstract: Geometric priors are often used to enhance 3D reconstruction. With many
    smartphones featuring low-resolution depth sensors and the prevalence of off-the-shelf
    monocular geometry estimators, incorporating geometric priors as regularization
    signals has become common in 3D vision tasks. However, the accuracy of depth estimates
    from mobile devices is typically poor for highly detailed geometry, and monocular
    estimators often suffer from poor multi-view consistency and precision. In this
    work, we propose an approach for joint surface depth and normal refinement of
    Gaussian Splatting methods for accurate 3D reconstruction of indoor scenes. We
    develop supervision strategies that adaptively filters low-quality depth and normal
    estimates by comparing the consistency of the priors during optimization. We mitigate
    regularization in regions where prior estimates have high uncertainty or ambiguities.
    Our filtering strategy and optimization design demonstrate significant improvements
    in both mesh estimation and novel-view synthesis for both 3D and 2D Gaussian Splatting-based
    methods on challenging indoor room datasets. Furthermore, we explore the use of
    alternative meshing strategies for finer geometry extraction. We develop a scale-aware
    meshing strategy inspired by TSDF and octree-based isosurface extraction, which
    recovers finer details from Gaussian models compared to other commonly used open-source
    meshing tools. Our code is released in https://xuqianren.github.io/ags_mesh_website/.
  project_page: https://xuqianren.github.io/ags_mesh_website/
  paper: https://arxiv.org/pdf/2411.19271
  code: https://github.com/XuqianRen/AGS_Mesh
  video: null
  tags:
  - Code
  - Meshing
  - Project
  thumbnail: assets/thumbnails/ren2024agsmesh.jpg
  publication_date: '2024-11-28T17:04:32+00:00'
- id: wimmer2024gaussianstolife
  title: 'Gaussians-to-Life: Text-Driven Animation of 3D Gaussian Splatting Scenes'
  authors: Thomas Wimmer, Michael Oechsle, Michael Niemeyer, Federico Tombari
  year: '2024'
  abstract: 'State-of-the-art novel view synthesis methods achieve impressive results
    for multi-view captures of static 3D scenes. However, the reconstructed scenes
    still lack "liveliness," a key component for creating engaging 3D experiences.
    Recently, novel video diffusion models generate realistic videos with complex
    motion and enable animations of 2D images, however they cannot naively be used
    to animate 3D scenes as they lack multi-view consistency. To breathe life into
    the static world, we propose Gaussians2Life, a method for animating parts of high-quality
    3D scenes in a Gaussian Splatting representation. Our key idea is to leverage
    powerful video diffusion models as the generative component of our model and to
    combine these with a robust technique to lift 2D videos into meaningful 3D motion.
    We find that, in contrast to prior work, this enables realistic animations of
    complex, pre-existing 3D scenes and further enables the animation of a large variety
    of object classes, while related work is mostly focused on prior-based character
    animation, or single 3D objects. Our model enables the creation of consistent,
    immersive 3D experiences for arbitrary scenes.

    '
  project_page: https://wimmerth.github.io/gaussians2life.html
  paper: https://arxiv.org/pdf/2411.19233.pdf
  code: https://github.com/wimmerth/gaussians2life
  video: null
  tags:
  - Code
  - Diffusion
  - Dynamic
  - Project
  thumbnail: assets/thumbnails/wimmer2024gaussianstolife.jpg
  publication_date: '2024-11-28T16:01:58+00:00'
  date_source: arxiv
- id: xu2024supergaussians
  title: 'SuperGaussians: Enhancing Gaussian Splatting Using Primitives with Spatially
    Varying Colors'
  authors: Rui Xu, Wenyue Chen, Jiepeng Wang, Yuan Liu, Peng Wang, Lin Gao, Shiqing
    Xin, Taku Komura, Xin Li, Wenping Wang
  year: '2024'
  abstract: Gaussian Splattings demonstrate impressive results in multi-view reconstruction
    based on Gaussian explicit representations. However, the current Gaussian primitives
    only have a single view-dependent color and an opacity to represent the appearance
    and geometry of the scene, resulting in a non-compact representation. In this
    paper, we introduce a new method called SuperGaussians that utilizes spatially
    varying colors and opacity in a single Gaussian primitive to improve its representation
    ability. We have implemented bilinear interpolation, movable kernels, and even
    tiny neural networks as spatially varying functions. Quantitative and qualitative
    experimental results demonstrate that all three functions outperform the baseline,
    with the best movable kernels achieving superior novel view synthesis performance
    on multiple datasets, highlighting the strong potential of spatially varying functions.
  project_page: https://ruixu.me/html/SuperGaussians/index.html
  paper: https://arxiv.org/pdf/2411.18966
  code: https://github.com/Xrvitd/SuperGaussians
  video: https://www.youtube.com/watch?v=K0liSjZGnIY&feature=youtu.be
  tags:
  - Code
  - Project
  - Rendering
  - Video
  thumbnail: assets/thumbnails/xu2024supergaussians.jpg
  publication_date: '2024-11-28T07:36:22+00:00'
- id: chao2024textured
  title: Textured Gaussians for Enhanced 3D Scene Appearance Modeling
  authors: Brian Chao, Hung-Yu Tseng, Lorenzo Porzi, Chen Gao, Tuotuo Li, Qinbo Li,
    Ayush Saraf, Jia-Bin Huang, Johannes Kopf, Gordon Wetzstein, Changil Kim
  year: '2024'
  abstract: '3D Gaussian Splatting (3DGS) has recently emerged as a state-of-the-art
    3D reconstruction and rendering technique due to its high-quality results and
    fast training and rendering time. However, pixels covered by the same Gaussian
    are always shaded in the same color up to a Gaussian falloff scaling factor. Furthermore,
    the finest geometric detail any individual Gaussian can represent is a simple
    ellipsoid. These properties of 3DGS greatly limit the expressivity of individual
    Gaussian primitives. To address these issues, we draw inspiration from texture
    and alpha mapping in traditional graphics and integrate it with 3DGS. Specifically,
    we propose a new generalized Gaussian appearance representation that augments
    each Gaussian with alpha~(A), RGB, or RGBA texture maps to model spatially varying
    color and opacity across the extent of each Gaussian. As such, each Gaussian can
    represent a richer set of texture patterns and geometric structures, instead of
    just a single color and ellipsoid as in naive Gaussian Splatting. Surprisingly,
    we found that the expressivity of Gaussians can be greatly improved by using alpha-only
    texture maps, and further augmenting Gaussians with RGB texture maps achieves
    the highest expressivity. We validate our method on a wide variety of standard
    benchmark datasets and our own custom captures at both the object and scene levels.
    We demonstrate image quality improvements over existing methods while using a
    similar or lower number of Gaussians.

    '
  project_page: https://textured-gaussians.github.io/
  paper: https://arxiv.org/pdf/2411.18625.pdf
  code: null
  video: null
  tags:
  - In the Wild
  - Project
  - Rendering
  - Texturing
  thumbnail: assets/thumbnails/chao2024textured.jpg
  publication_date: '2024-11-27T18:59:59+00:00'
  date_source: arxiv
- id: wu2024cat4d
  title: 'CAT4D: Create Anything in 4D with Multi-View Video Diffusion Models'
  authors: Rundi Wu, Ruiqi Gao, Ben Poole, Alex Trevithick, Changxi Zheng, Jonathan
    T. Barron, Aleksander Holynski
  year: '2024'
  abstract: 'We present CAT4D, a method for creating 4D (dynamic 3D) scenes from monocular
    video. CAT4D leverages a multi-view video diffusion model trained on a diverse
    combination of datasets to enable novel view synthesis at any specified camera
    poses and timestamps. Combined with a novel sampling approach, this model can
    transform a single monocular video into a multi-view video, enabling robust 4D
    reconstruction via optimization of a deformable 3D Gaussian representation. We
    demonstrate competitive performance on novel view synthesis and dynamic scene
    reconstruction benchmarks, and highlight the creative capabilities for 4D scene
    generation from real or generated videos. See our project page for results and
    interactive demos: https://cat-4d.github.io/.

    '
  project_page: https://cat-4d.github.io/
  paper: https://arxiv.org/pdf/2411.18613.pdf
  code: null
  video: null
  tags:
  - Diffusion
  - Dynamic
  - Project
  thumbnail: assets/thumbnails/wu2024cat4d.jpg
  publication_date: '2024-11-27T18:57:16+00:00'
  date_source: arxiv
- id: kang2024selfsplat
  title: 'SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian Splatting'
  authors: Gyeongjin Kang, Jisang Yoo, Jihyeon Park, Seungtae Nam, Hyeonsoo Im, Sangheon
    Shin, Sangpil Kim, Eunbyung Park
  year: '2024'
  abstract: 'We propose SelfSplat, a novel 3D Gaussian Splatting model designed to
    perform pose-free and 3D prior-free generalizable 3D reconstruction from unposed
    multi-view images. These settings are inherently ill-posed due to the lack of
    ground-truth data, learned geometric information, and the need to achieve accurate
    3D reconstruction without finetuning, making it difficult for conventional methods
    to achieve high-quality results. Our model addresses these challenges by effectively
    integrating explicit 3D representations with self-supervised depth and pose estimation
    techniques, resulting in reciprocal improvements in both pose accuracy and 3D
    reconstruction quality. Furthermore, we incorporate a matching-aware pose estimation
    network and a depth refinement module to enhance geometry consistency across views,
    ensuring more accurate and stable 3D reconstructions. To present the performance
    of our method, we evaluated it on large-scale real-world datasets, including RealEstate10K,
    ACID, and DL3DV. SelfSplat achieves superior results over previous state-of-the-art
    methods in both appearance and geometry quality, also demonstrates strong cross-dataset
    generalization capabilities. Extensive ablation studies and analysis also validate
    the effectiveness of our proposed methods. Code and pretrained models are available
    at https://gynjn.github.io/selfsplat/

    '
  project_page: https://gynjn.github.io/selfsplat/
  paper: https://arxiv.org/pdf/2411.17190.pdf
  code: https://github.com/Gynjn/selfsplat
  video: null
  tags:
  - Code
  - Feed-Forward
  - Poses
  - Project
  thumbnail: assets/thumbnails/kang2024selfsplat.jpg
  publication_date: '2024-11-26T08:01:50+00:00'
  date_source: arxiv
- id: flynn2024quark
  title: 'Quark: Real-time, High-resolution, and General Neural View Synthesis'
  authors: John Flynn, Michael Broxton, Lukas Murmann, Lucy Chai, Matthew DuVall,
    Clément Godard, Kathryn Heal, Srinivas Kaza, Stephen Lombardi, Xuan Luo, Supreeth
    Achar, Kira Prabhu, Tiancheng Sun, Lynn Tsai, Ryan Overbeck
  year: '2024'
  abstract: 'We present a novel neural algorithm for performing high-quality, high-resolution,
    real-time novel view synthesis. From a sparse set of input RGB images or videos
    streams, our network both reconstructs the 3D scene and renders novel views at
    1080p resolution at 30fps on an NVIDIA A100. Our feed-forward network generalizes
    across a wide variety of datasets and scenes and produces state-of-the-art quality
    for a real-time method. Our quality approaches, and in some cases surpasses, the
    quality of some of the top offline methods. In order to achieve these results
    we use a novel combination of several key concepts, and tie them together into
    a cohesive and effective algorithm. We build on previous works that represent
    the scene using semi-transparent layers and use an iterative learned render-and-refine
    approach to improve those layers. Instead of flat layers, our method reconstructs
    layered depth maps (LDMs) that efficiently represent scenes with complex depth
    and occlusions. The iterative update steps are embedded in a multi-scale, UNet-style
    architecture to perform as much compute as possible at reduced resolution. Within
    each update step, to better aggregate the information from multiple input views,
    we use a specialized Transformer-based network component. This allows the majority
    of the per-input image processing to be performed in the input image space, as
    opposed to layer space, further increasing efficiency. Finally, due to the real-time
    nature of our reconstruction and rendering, we dynamically create and discard
    the internal 3D geometry for each frame, generating the LDM for each view. Taken
    together, this produces a novel and effective algorithm for view synthesis. Through
    extensive evaluation, we demonstrate that we achieve state-of-the-art quality
    at real-time rates. Project page: https://quark-3d.github.io/

    '
  project_page: https://arxiv.org/abs/2411.16680
  paper: https://arxiv.org/pdf/2411.16680.pdf
  code: null
  video: https://youtu.be/w9Oqhaqbsyo?si=ochKsH7fuxgM5BFg
  tags:
  - Feed-Forward
  - Project
  - Rendering
  - Video
  thumbnail: assets/thumbnails/flynn2024quark.jpg
  publication_date: '2024-11-25T18:59:50+00:00'
  date_source: arxiv
- id: hess2024splatad
  title: 'SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting
    for Autonomous Driving'
  authors: Georg Hess, Carl Lindström, Maryam Fatemi, Christoffer Petersson, Lennart
    Svensson
  year: '2024'
  abstract: Ensuring the safety of autonomous robots, such as self-driving vehicles,
    requires extensive testing across diverse driving scenarios. Simulation is a key
    ingredient for conducting such testing in a cost-effective and scalable way. Neural
    rendering methods have gained popularity, as they can build simulation environments
    from collected logs in a data-driven manner. However, existing neural radiance
    field (NeRF) methods for sensor-realistic rendering of camera and lidar data suffer
    from low rendering speeds, limiting their applicability for large-scale testing.
    While 3D Gaussian Splatting (3DGS) enables real-time rendering, current methods
    are limited to camera data and are unable to render lidar data essential for autonomous
    driving. To address these limitations, we propose SplatAD, the first 3DGS-based
    method for realistic, real-time rendering of dynamic scenes for both camera and
    lidar data. SplatAD accurately models key sensor-specific phenomena such as rolling
    shutter effects, lidar intensity, and lidar ray dropouts, using purpose-built
    algorithms to optimize rendering efficiency. Evaluation across three autonomous
    driving datasets demonstrates that SplatAD achieves state-of-the-art rendering
    quality with up to +2 PSNR for NVS and +3 PSNR for reconstruction while increasing
    rendering speed over NeRF-based methods by an order of magnitude.
  project_page: https://research.zenseact.com/publications/splatad/
  paper: https://arxiv.org/pdf/2411.16816
  code: null
  video: null
  tags:
  - Autonomous Driving
  - Project
  thumbnail: assets/thumbnails/hess2024splatad.jpg
  publication_date: '2024-11-25T16:18:22+00:00'
- id: zhang2024quadratic
  title: Quadratic Gaussian Splatting for Efficient and Detailed Surface Reconstruction
  authors: Ziyu Zhang, Binbin Huang, Hanqing Jiang, Liyang Zhou, Xiaojun Xiang, Shunhan
    Shen
  year: '2024'
  abstract: 'Recently, 3D Gaussian Splatting (3DGS) has attracted attention for its
    superior rendering quality and speed over Neural Radiance Fields (NeRF). To address
    3DGS''s limitations in surface representation, 2D Gaussian Splatting (2DGS) introduced
    disks as scene primitives to model and reconstruct geometries from multi-view
    images, offering view-consistent geometry. However, the disk''s first-order linear
    approximation often leads to over-smoothed results. We propose Quadratic Gaussian
    Splatting (QGS), a novel method that replaces disks with quadric surfaces, enhancing
    geometric fitting, whose code will be open-sourced. QGS defines Gaussian distributions
    in non-Euclidean space, allowing primitives to capture more complex textures.
    As a second-order surface approximation, QGS also renders spatial curvature to
    guide the normal consistency term, to effectively reduce over-smoothing. Moreover,
    QGS is a generalized version of 2DGS that achieves more accurate and detailed
    reconstructions, as verified by experiments on DTU and TNT, demonstrating its
    effectiveness in surpassing current state-of-the-art methods in geometry reconstruction.
    Our code willbe released as open source.

    '
  project_page: https://quadraticgs.github.io/QGS/
  paper: https://arxiv.org/pdf/2411.16392.pdf
  code: https://github.com/QuadraticGS/QGS
  video: https://quadraticgs.github.io/QGS/assets/QGS_web.mp4
  tags:
  - Code
  - Meshing
  - Project
  - Video
  thumbnail: assets/thumbnails/zhang2024quadratic.jpg
  publication_date: '2024-11-25T13:55:00+00:00'
  date_source: arxiv
- id: zhang2024fate
  title: 'FATE: Full-head Gaussian Avatar with Textural Editing from Monocular Video'
  authors: Jiawei Zhang, Zijian Wu, Zhiyang Liang, Yicheng Gong, Dongfang Hu, Yao
    Yao, Xun Cao, Hao Zhu
  year: '2024'
  abstract: 'Reconstructing high-fidelity, animatable 3D head avatars from effortlessly
    captured monocular videos is a pivotal yet formidable challenge. Although significant
    progress has been made in rendering performance and manipulation capabilities,
    notable challenges remain, including incomplete reconstruction and inefficient
    Gaussian representation. To address these challenges, we introduce FATE, a novel
    method for reconstructing an editable full-head avatar from a single monocular
    video. FATE integrates a sampling-based densification strategy to ensure optimal
    positional distribution of points, improving rendering efficiency. A neural baking
    technique is introduced to convert discrete Gaussian representations into continuous
    attribute maps, facilitating intuitive appearance editing. Furthermore, we propose
    a universal completion framework to recover non-frontal appearance, culminating
    in a 360$^\circ$-renderable 3D head avatar. FATE outperforms previous approaches
    in both qualitative and quantitative evaluations, achieving state-of-the-art performance.
    To the best of our knowledge, FATE is the first animatable and 360$^\circ$ full-head
    monocular reconstruction method for a 3D head avatar. The code will be publicly
    released upon publication.

    '
  project_page: https://zjwfufu.github.io/FATE-page/
  paper: https://arxiv.org/pdf/2411.15604.pdf
  code: https://github.com/zjwfufu/FateAvatar
  video: null
  tags:
  - Avatar
  - Code
  - Dynamic
  - Editing
  - Monocular
  - Project
  - Texturing
  thumbnail: assets/thumbnails/zhang2024fate.jpg
  publication_date: '2024-11-23T16:47:48+00:00'
  date_source: arxiv
- id: blark2024splatsdf
  title: 'SplatSDF: Boosting Neural Implicit SDF via Gaussian Splatting Fusion'
  authors: Runfa Blark Li, Keito Suzuki, Bang Du, Ki Myung Brian Lee, Nikolay Atanasov,
    Truong Nguyen
  year: '2024'
  abstract: A signed distance function (SDF) is a useful representation for continuous-space
    geometry and many related operations, including rendering, collision checking,
    and mesh generation. Hence, reconstructing SDF from image observations accurately
    and efficiently is a fundamental problem. Recently, neural implicit SDF (SDF-NeRF)
    techniques, trained using volumetric rendering, have gained a lot of attention.
    Compared to earlier truncated SDF (TSDF) fusion algorithms that rely on depth
    maps and voxelize continuous space, SDF-NeRF enables continuous-space SDF reconstruction
    with better geometric and photometric accuracy. However, the accuracy and convergence
    speed of scene-level SDF reconstruction require further improvements for many
    applications. With the advent of 3D Gaussian Splatting (3DGS) as an explicit representation
    with excellent rendering quality and speed, several works have focused on improving
    SDF-NeRF by introducing consistency losses on depth and surface normals between
    3DGS and SDF-NeRF. However, loss-level connections alone lead to incremental improvements.
    We propose a novel neural implicit SDF called “SplatSDF” to fuse 3DGS and SDF-NeRF
    at an architecture level with significant boosts to geometric and photometric
    accuracy and convergence speed. Our SplatSDF relies on 3DGS as input only during
    training, and keeps the same complexity and efficiency as the original SDF-NeRF
    during inference. Our method outperforms state-of-the-art SDF-NeRF models on geometric
    and photometric evaluation by the time of submission.
  project_page: https://blarklee.github.io/splatsdf/
  paper: https://arxiv.org/pdf/2411.15468.pdf
  code: null
  video: null
  tags:
  - Meshing
  - Project
  thumbnail: assets/thumbnails/blark2024splatsdf.jpg
  publication_date: '2024-11-23T06:35:19+00:00'
- id: chou2024generating
  title: Generating 3D-Consistent Videos from Unposed Internet Photos
  authors: Gene Chou, Kai Zhang, Sai Bi, Hao Tan, Zexiang Xu, Fujun Luan, Bharath
    Hariharan, Noah Snavely
  year: '2024'
  abstract: 'We address the problem of generating videos from unposed internet photos.
    A handful of input images serve as keyframes, and our model interpolates between
    them to simulate a path moving between the cameras. Given random images, a model''s
    ability to capture underlying geometry, recognize scene identity, and relate frames
    in terms of camera position and orientation reflects a fundamental understanding
    of 3D structure and scene layout. However, existing video models such as Luma
    Dream Machine fail at this task. We design a self-supervised method that takes
    advantage of the consistency of videos and variability of multiview internet photos
    to train a scalable, 3D-aware video model without any 3D annotations such as camera
    parameters. We validate that our method outperforms all baselines in terms of
    geometric and appearance consistency. We also show our model benefits applications
    that enable camera control, such as 3D Gaussian Splatting. Our results suggest
    that we can scale up scene-level 3D learning using only 2D data such as videos
    and multiview internet photos.

    '
  project_page: https://genechou.com/kfcw/
  paper: https://arxiv.org/pdf/2411.13549.pdf
  code: null
  video: null
  tags:
  - Feed-Forward
  - In the Wild
  - Poses
  - Project
  - Transformer
  thumbnail: assets/thumbnails/chou2024generating.jpg
  publication_date: '2024-11-20T18:58:31+00:00'
  date_source: arxiv
- id: fang2024minisplatting2
  title: 'Mini-Splatting2: Building 360 Scenes within Minutes via Aggressive Gaussian
    Densification'
  authors: Guangchi Fang, Bing Wang
  year: '2024'
  abstract: 'In this study, we explore the essential challenge of fast scene optimization
    for Gaussian Splatting. Through a thorough analysis of the geometry modeling process,
    we reveal that dense point clouds can be effectively reconstructed early in optimization
    through Gaussian representations. This insight leads to our approach of aggressive
    Gaussian densification, which provides a more efficient alternative to conventional
    progressive densification methods. By significantly increasing the number of critical
    Gaussians, we enhance the model capacity to capture dense scene geometry at the
    early stage of optimization. This strategy is seamlessly integrated into the Mini-Splatting
    densification and simplification framework, enabling rapid convergence without
    compromising quality. Additionally, we introduce visibility culling within Gaussian
    Splatting, leveraging per-view Gaussian importance as precomputed visibility to
    accelerate the optimization process. Our Mini-Splatting2 achieves a balanced trade-off
    among optimization time, the number of Gaussians, and rendering quality, establishing
    a strong baseline for future Gaussian-Splatting-based works. Our work sets the
    stage for more efficient, high-quality 3D scene modeling in real-world applications,
    and the code will be made available no matter acceptance.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2411.12788.pdf
  code: https://github.com/fatPeter/mini-splatting2
  video: null
  tags:
  - Acceleration
  - Code
  - Densification
  thumbnail: assets/thumbnails/fang2024minisplatting2.jpg
  publication_date: '2024-11-19T11:47:40+00:00'
  date_source: arxiv
- id: zhou2024gpsgaussian
  title: 'GPS-Gaussian+: Generalizable Pixel-wise 3D Gaussian Splatting for Real-Time
    Human-Scene Rendering from Sparse Views'
  authors: Boyao Zhou, Shunyuan Zheng, Hanzhang Tu, Ruizhi Shao, Boning Liu, Shengping
    Zhang, Liqiang Nie, Yebin Liu
  year: '2024'
  abstract: 'Differentiable rendering techniques have recently shown promising results
    for free-viewpoint video synthesis of characters. However, such methods, either
    Gaussian Splatting or neural implicit rendering, typically necessitate per-subject
    optimization which does not meet the requirement of real-time rendering in an
    interactive application. We propose a generalizable Gaussian Splatting approach
    for high-resolution image rendering under a sparse-view camera setting. To this
    end, we introduce Gaussian parameter maps defined on the source views and directly
    regress Gaussian properties for instant novel view synthesis without any fine-tuning
    or optimization. We train our Gaussian parameter regression module on human-only
    data or human-scene data, jointly with a depth estimation module to lift 2D parameter
    maps to 3D space. The proposed framework is fully differentiable with both depth
    and rendering supervision or with only rendering supervision. We further introduce
    a regularization term and an epipolar attention mechanism to preserve geometry
    consistency between two source views, especially when neglecting depth supervision.
    Experiments on several datasets demonstrate that our method outperforms state-of-the-art
    methods while achieving an exceeding rendering speed.

    '
  project_page: https://yaourtb.github.io/GPS-Gaussian+
  paper: https://arxiv.org/pdf/2411.11363.pdf
  code: null
  video: null
  tags:
  - Acceleration
  - Dynamic
  - Project
  - Rendering
  thumbnail: assets/thumbnails/zhou2024gpsgaussian.jpg
  publication_date: '2024-11-18T08:18:44+00:00'
  date_source: arxiv
- id: kong2024dgsslam
  title: 'DGS-SLAM: Gaussian Splatting SLAM in Dynamic Environment'
  authors: Mangyu Kong, Jaewon Lee, Seongwon Lee, Euntai Kim
  year: '2024'
  abstract: We introduce Dynamic Gaussian Splatting SLAM (DGS-SLAM), the first dynamic
    SLAM framework built on the foundation of Gaussian Splatting. While recent advancements
    in dense SLAM have leveraged Gaussian Splatting to enhance scene representation,
    most approaches assume a static environment, making them vulnerable to photometric
    and geometric inconsistencies caused by dynamic objects. To address these challenges,
    we integrate Gaussian Splatting SLAM with a robust filtering process to handle
    dynamic objects throughout the entire pipeline, including Gaussian insertion and
    keyframe selection. Within this framework, to further improve the accuracy of
    dynamic object removal, we introduce a robust mask generation method that enforces
    photometric consistency across keyframes, reducing noise from inaccurate segmentation
    and artifacts such as shadows. Additionally, we propose the loop-aware window
    selection mechanism, which utilizes unique keyframe IDs of 3D Gaussians to detect
    loops between the current and past frames, facilitating joint optimization of
    the current camera poses and the Gaussian map. DGS-SLAM achieves state-of-the-art
    performance in both camera tracking and novel view synthesis on various dynamic
    SLAM benchmarks, proving its effectiveness in handling real-world dynamic scenes.
  project_page: null
  paper: https://arxiv.org/pdf/2411.10722
  code: null
  video: https://youtu.be/Mq3qZTTcN3E?feature=shared
  tags:
  - SLAM
  - Video
  thumbnail: assets/thumbnails/kong2024dgsslam.jpg
  publication_date: '2024-11-16T07:02:46+00:00'
- id: tang2024spars3r
  title: 'SPARS3R: Semantic Prior Alignment and Regularization for Sparse 3D Reconstruction'
  authors: Yutao Tang, Yuxiang Guo, Deming Li, Cheng Peng
  year: '2024'
  abstract: 'Recent efforts in Gaussian-Splat-based Novel View Synthesis can achieve
    photorealistic rendering; however, such capability is limited in sparse-view scenarios
    due to sparse initialization and over-fitting floaters. Recent progress in depth
    estimation and alignment can provide dense point cloud with few views; however,
    the resulting pose accuracy is suboptimal. In this work, we present SPARS3R, which
    combines the advantages of accurate pose estimation from Structure-from-Motion
    and dense point cloud from depth estimation. To this end, SPARS3R first performs
    a Global Fusion Alignment process that maps a prior dense point cloud to a sparse
    point cloud from Structure-from-Motion based on triangulated correspondences.
    RANSAC is applied during this process to distinguish inliers and outliers. SPARS3R
    then performs a second, Semantic Outlier Alignment step, which extracts semantically
    coherent regions around the outliers and performs local alignment in these regions.
    Along with several improvements in the evaluation process, we demonstrate that
    SPARS3R can achieve photorealistic rendering with sparse images and significantly
    outperforms existing approaches.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2411.12592.pdf
  code: null
  video: null
  tags:
  - 3ster-based
  - Poses
  - Sparse
  thumbnail: assets/thumbnails/tang2024spars3r.jpg
  publication_date: '2024-11-15T20:06:15+00:00'
  date_source: arxiv
- id: svitov2024billboard
  title: 'BillBoard Splatting (BBSplat): Learnable Textured Primitives for Novel View
    Synthesis'
  authors: David Svitov, Pietro Morerio, Lourdes Agapito, Alessio Del Bue
  year: '2024'
  abstract: 'We present billboard Splatting (BBSplat) - a novel approach for 3D scene
    representation based on textured geometric primitives. BBSplat represents the
    scene as a set of optimizable textured planar primitives with learnable RGB textures
    and alpha-maps to control their shape. BBSplat primitives can be used in any Gaussian
    Splatting pipeline as drop-in replacements for Gaussians. Our method''s qualitative
    and quantitative improvements over 3D and 2D Gaussians are most noticeable when
    fewer primitives are used, when BBSplat achieves over 1200 FPS. Our novel regularization
    term encourages textures to have a sparser structure, unlocking an efficient compression
    that leads to a reduction in storage space of the model. Our experiments show
    the efficiency of BBSplat on standard datasets of real indoor and outdoor scenes
    such as Tanks&Temples, DTU, and Mip-NeRF-360. We demonstrate improvements on PSNR,
    SSIM, and LPIPS metrics compared to the state-of-the-art, especially for the case
    when fewer primitives are used, which, on the other hand, leads to up to 2 times
    inference speed improvement for the same rendering quality.

    '
  project_page: https://david-svitov.github.io/BBSplat_project_page/
  paper: https://arxiv.org/pdf/2411.08508.pdf
  code: https://github.com/david-svitov/BBSplat
  video: https://youtu.be/uRM7WFo5vVg
  tags:
  - Code
  - Optimization
  - Project
  - Texturing
  - Video
  thumbnail: assets/thumbnails/svitov2024billboard.jpg
  publication_date: '2024-11-13T10:43:39+00:00'
  date_source: arxiv
- id: wang2024mbaslam
  title: 'MBA-SLAM: Motion Blur Aware Dense Visual SLAM with Radiance Fields Representation'
  authors: Peng Wang, Lingzhe Zhao, Yin Zhang, Shiyu Zhao, Peidong Liu
  year: '2024'
  abstract: Emerging 3D scene representations, such as Neural Radiance Fields (NeRF)
    and 3D Gaussian Splatting (3DGS), have demonstrated their effectiveness in Simultaneous
    Localization and Mapping (SLAM) for photo-realistic rendering, particularly when
    using high-quality video sequences as input. However, existing methods struggle
    with motion-blurred frames, which are common in real-world scenarios like low-light
    or long-exposure conditions. This often results in a significant reduction in
    both camera localization accuracy and map reconstruction quality. To address this
    challenge, we propose a dense visual SLAM pipeline (i.e. MBA-SLAM) to handle severe
    motion-blurred inputs. Our approach integrates an efficient motion blur-aware
    tracker with either neural radiance fields or Gaussian Splatting based mapper.
    By accurately modeling the physical image formation process of motion-blurred
    images, our method simultaneously learns 3D scene representation and estimates
    the cameras' local trajectory during exposure time, enabling proactive compensation
    for motion blur caused by camera movement. In our experiments, we demonstrate
    that MBA-SLAM surpasses previous state-of-the-art methods in both camera localization
    and map reconstruction, showcasing superior performance across a range of datasets,
    including synthetic and real datasets featuring sharp images as well as those
    affected by motion blur, highlighting the versatility and robustness of our approach.
  project_page: https://wangpeng000.github.io/MBA-SLAM/
  paper: https://arxiv.org/pdf/2411.08279
  code: null
  video: null
  tags:
  - Project
  - SLAM
  thumbnail: assets/thumbnails/wang2024mbaslam.jpg
  publication_date: '2024-11-13T01:38:06+00:00'
- id: zhang2024gaussianspa
  title: 'GaussianSpa: An "Optimizing-Sparsifying" Simplification Framework for Compact
    and High-Quality 3D Gaussian Splatting'
  authors: Yangming Zhang, Wenqi Jia, Wei Niu, Miao Yin
  year: '2024'
  abstract: '3D Gaussian Splatting (3DGS) has emerged as a mainstream for novel view
    synthesis, leveraging continuous aggregations of Gaussian functions to model scene
    geometry. However, 3DGS suffers from substantial memory requirements to store
    the multitude of Gaussians, hindering its practicality. To address this challenge,
    we introduce GaussianSpa, an optimization-based simplification framework for compact
    and high-quality 3DGS. Specifically, we formulate the simplification as an optimization
    problem associated with the 3DGS training. Correspondingly, we propose an efficient
    "optimizing-sparsifying" solution that alternately solves two independent sub-problems,
    gradually imposing strong sparsity onto the Gaussians in the training process.
    Our comprehensive evaluations on various datasets show the superiority of GaussianSpa
    over existing state-of-the-art approaches. Notably, GaussianSpa achieves an average
    PSNR improvement of 0.9 dB on the real-world Deep Blending dataset with 10$\times$
    fewer Gaussians compared to the vanilla 3DGS. Our project page is available at
    https://gaussianspa.github.io/.

    '
  project_page: https://gaussianspa.github.io/
  paper: https://arxiv.org/pdf/2411.06019.pdf
  code: null
  video: null
  tags:
  - Compression
  - Densification
  - Project
  thumbnail: assets/thumbnails/zhang2024gaussianspa.jpg
  publication_date: '2024-11-09T00:38:06+00:00'
  date_source: arxiv
- id: lu20243dgscd
  title: '3DGS-CD: 3D Gaussian Splatting-based Change Detection for Physical Object
    Rearrangement'
  authors: Ziqi Lu, Jianbo Ye, John Leonard
  year: '2024'
  abstract: 'We present 3DGS-CD, the first 3D Gaussian Splatting (3DGS)-based method
    for detecting physical object rearrangements in 3D scenes. Our approach estimates
    3D object-level changes by comparing two sets of unaligned images taken at different
    times. Leveraging 3DGS''s novel view rendering and EfficientSAM''s zero-shot segmentation
    capabilities, we detect 2D object-level changes, which are then associated and
    fused across views to estimate 3D changes. Our method can detect changes in cluttered
    environments using sparse post-change images within as little as 18s, using as
    few as a single new image. It does not rely on depth input, user instructions,
    object classes, or object models -- An object is recognized simply if it has been
    re-arranged. Our approach is evaluated on both public and self-collected real-world
    datasets, achieving up to 14% higher accuracy and three orders of magnitude faster
    performance compared to the state-of-the-art radiance-field-based change detection
    method. This significant performance boost enables a broad range of downstream
    applications, where we highlight three key use cases: object reconstruction, robot
    workspace reset, and 3DGS model update. Our code and data will be made available
    at https://github.com/520xyxyzq/3DGS-CD.'
  project_page: null
  paper: https://arxiv.org/pdf/2411.03706
  code: null
  video: null
  tags:
  - Robotics
  thumbnail: assets/thumbnails/lu20243dgscd.jpg
  publication_date: '2024-11-06T07:08:41+00:00'
- id: liu2024citygaussianv2
  title: 'CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for
    Large-Scale Scenes'
  authors: Yang Liu, Chuanchen Luo, Zhongkai Mao, Junran Peng, Zhaoxiang Zhang
  year: '2024'
  abstract: Recently, 3D Gaussian Splatting (3DGS) has revolutionized radiance field
    reconstruction, manifesting efficient and high-fidelity novel view synthesis.
    However, accurately representing surfaces, especially in large and complex scenarios,
    remains a significant challenge due to the unstructured nature of 3DGS. In this
    paper, we present CityGaussianV2, a novel approach for large-scale scene reconstruction
    that addresses critical challenges related to geometric accuracy and efficiency.
    Building on the favorable generalization capabilities of 2D Gaussian Splatting
    (2DGS), we address its convergence and scalability issues. Specifically, we implement
    a decomposed-gradient-based densification and depth regression technique to eliminate
    blurry artifacts and accelerate convergence. To scale up, we introduce an elongation
    filter that mitigates Gaussian count explosion caused by 2DGS degeneration. Furthermore,
    we optimize the CityGaussian pipeline for parallel training, achieving up to 10x
    compression, at least 25% savings in training time, and a 50% decrease in memory
    usage. We also established standard geometry benchmarks under large-scale scenes.
    Experimental results demonstrate that our method strikes a promising balance between
    visual quality, geometric accuracy, as well as storage and training costs.
  project_page: https://dekuliutesla.github.io/CityGaussianV2/
  paper: https://arxiv.org/pdf/2411.00771
  code: https://github.com/DekuLiuTesla/CityGaussian
  video: null
  tags:
  - Code
  - Large-Scale
  - Meshing
  - Project
  thumbnail: assets/thumbnails/liu2024citygaussianv2.jpg
  publication_date: '2024-11-01T17:59:31+00:00'
- id: zubair2024neural
  title: 'Neural Fields in Robotics: A Survey'
  authors: Muhammad Zubair Irshad, Mauro Comi, Yen-Chen Lin, Nick Heppert, Abhinav
    Valada, Zsolt Kira, Rares Ambrus, Johnathan Trembley
  year: '2024'
  abstract: 'Neural Fields have emerged as a transformative approach for 3D scene
    representation in computer vision and robotics, enabling accurate inference of
    geometry, 3D semantics, and dynamics from posed 2D data. Leveraging differentiable
    rendering, Neural Fields encompass both continuous implicit and explicit neural
    representations enabling high-fidelity 3D reconstruction, integration of multi-modal
    sensor data, and generation of novel viewpoints. This survey explores their applications
    in robotics, emphasizing their potential to enhance perception, planning, and
    control. Their compactness, memory efficiency, and differentiability, along with
    seamless integration with foundation and generative models, make them ideal for
    real-time applications, improving robot adaptability and decision-making. This
    paper provides a thorough review of Neural Fields in robotics, categorizing applications
    across various domains and evaluating their strengths and limitations, based on
    over 200 papers. First, we present four key Neural Fields frameworks: Occupancy
    Networks, Signed Distance Fields, Neural Radiance Fields, and Gaussian Splatting.
    Second, we detail Neural Fields'' applications in five major robotics domains:
    pose estimation, manipulation, navigation, physics, and autonomous driving, highlighting
    key works and discussing takeaways and open challenges. Finally, we outline the
    current limitations of Neural Fields in robotics and propose promising directions
    for future research.'
  project_page: null
  paper: https://arxiv.org/pdf/2410.20220
  code: null
  video: null
  tags:
  - Review
  - Robotics
  thumbnail: assets/thumbnails/zubair2024neural.jpg
  publication_date: '2024-10-26T16:26:41+00:00'
- id: zhou2024diffgs
  title: 'DiffGS: Functional Gaussian Splatting Diffusion'
  authors: Junsheng Zhou, Weiqi Zhang, Yu-Shen Liu
  year: '2024'
  abstract: '3D Gaussian Splatting (3DGS) has shown convincing performance in rendering
    speed and fidelity, yet the generation of Gaussian Splatting remains a challenge
    due to its discreteness and unstructured nature. In this work, we propose DiffGS,
    a general Gaussian generator based on latent diffusion models. DiffGS is a powerful
    and efficient 3D generative model which is capable of generating Gaussian primitives
    at arbitrary numbers for high-fidelity rendering with rasterization. The key insight
    is to represent Gaussian Splatting in a disentangled manner via three novel functions
    to model Gaussian probabilities, colors and transforms. Through the novel disentanglement
    of 3DGS, we represent the discrete and unstructured 3DGS with continuous Gaussian
    Splatting functions, where we then train a latent diffusion model with the target
    of generating these Gaussian Splatting functions both unconditionally and conditionally.
    Meanwhile, we introduce a discretization algorithm to extract Gaussians at arbitrary
    numbers from the generated functions via octree-guided sampling and optimization.
    We explore DiffGS for various tasks, including unconditional generation, conditional
    generation from text, image, and partial 3DGS, as well as Point-to-Gaussian generation.
    We believe that DiffGS provides a new direction for flexibly modeling and generating
    Gaussian Splatting.

    '
  project_page: https://junshengzhou.github.io/DiffGS/
  paper: https://arxiv.org/pdf/2410.19657.pdf
  code: https://github.com/weiqi-zhang/DiffGS
  video: null
  tags:
  - Code
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/zhou2024diffgs.jpg
  publication_date: '2024-10-25T16:08:08+00:00'
  date_source: arxiv
- id: fan2024large
  title: 'Large Spatial Model: End-to-end Unposed Images to Semantic 3D'
  authors: Zhiwen Fan, Jian Zhang, Wenyan Cong, Peihao Wang, Renjie Li, Kairun Wen,
    Shijie Zhou, Achuta Kadambi, Zhangyang Wang, Danfei Xu, Boris Ivanovic, Marco
    Pavone, Yue Wang
  year: '2024'
  abstract: 'Reconstructing and understanding 3D structures from a limited number
    of images is a well-established problem in computer vision. Traditional methods
    usually break this task into multiple subtasks, each requiring complex transformations
    between different data representations. For instance, dense reconstruction through
    Structure-from-Motion (SfM) involves converting images into key points, optimizing
    camera parameters, and estimating structures. Afterward, accurate sparse reconstructions
    are required for further dense modeling, which is subsequently fed into task-specific
    neural networks. This multi-step process results in considerable processing time
    and increased engineering complexity. In this work, we present the Large Spatial
    Model (LSM), which processes unposed RGB images directly into semantic radiance
    fields. LSM simultaneously estimates geometry, appearance, and semantics in a
    single feed-forward operation, and it can generate versatile label maps by interacting
    with language at novel viewpoints. Leveraging a Transformer-based architecture,
    LSM integrates global geometry through pixel-aligned point maps. To enhance spatial
    attribute regression, we incorporate local context aggregation with multi-scale
    fusion, improving the accuracy of fine local details. To tackle the scarcity of
    labeled 3D semantic data and enable natural language-driven scene manipulation,
    we incorporate a pre-trained 2D language-based segmentation model into a 3D-consistent
    semantic feature field. An efficient decoder then parameterizes a set of semantic
    anisotropic Gaussians, facilitating supervised end-to-end learning. Extensive
    experiments across various tasks show that LSM unifies multiple 3D vision tasks
    directly from unposed images, achieving real-time semantic 3D reconstruction for
    the first time.

    '
  project_page: https://largespatialmodel.github.io/
  paper: https://arxiv.org/pdf/2410.18956.pdf
  code: https://github.com/NVlabs/LSM
  video: https://largespatialmodel.github.io/static/videos/LSM_demo1.mp4
  tags:
  - Code
  - Feed-Forward
  - Poses
  - Project
  - Segmentation
  - Video
  thumbnail: assets/thumbnails/fan2024large.jpg
  publication_date: '2024-10-24T17:54:42+00:00'
  date_source: arxiv
- id: hou2024sortfree
  title: Sort-free Gaussian Splatting via Weighted Sum Rendering
  authors: Qiqi Hou, Randall Rauwendaal, Zifeng Li, Hoang Le, Farzad Farhadzadeh,
    Fatih Porikli, Alexei Bourd, Amir Said
  year: '2024'
  abstract: 'Recently, 3D Gaussian Splatting (3DGS) has emerged as a significant advancement
    in 3D scene reconstruction, attracting considerable attention due to its ability
    to recover high-fidelity details while maintaining low complexity. Despite the
    promising results achieved by 3DGS, its rendering performance is constrained by
    its dependence on costly non-commutative alpha-blending operations. These operations
    mandate complex view dependent sorting operations that introduce computational
    overhead, especially on the resource-constrained platforms such as mobile phones.
    In this paper, we propose Weighted Sum Rendering, which approximates alpha blending
    with weighted sums, thereby removing the need for sorting. This simplifies implementation,
    delivers superior performance, and eliminates the "popping" artifacts caused by
    sorting. Experimental results show that optimizing a generalized Gaussian splatting
    formulation to the new differentiable rendering yields competitive image quality.
    The method was implemented and tested in a mobile device GPU, achieving on average
    $1.23\times$ faster rendering.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2410.18931.pdf
  code: null
  video: null
  tags:
  - Acceleration
  - Rendering
  thumbnail: assets/thumbnails/hou2024sortfree.jpg
  publication_date: '2024-10-24T17:18:01+00:00'
  date_source: arxiv
- id: lee2024fully
  title: Fully Explicit Dynamic Gaussian Splatting
  authors: Junoh Lee, Changyeon Won, HyunJun Jung, Inhwan Bae, Hae-Gon Jeon
  year: '2024'
  abstract: 3D Gaussian Splatting has shown fast and high-quality rendering results
    in static scenes by leveraging dense 3D prior and explicit representations. Unfortunately,
    the benefits of the prior and representation do not involve novel view synthesis
    for dynamic motions. Ironically, this is because the main barrier is the reliance
    on them, which requires increasing training and rendering times to account for
    dynamic motions. In this paper, we design a \Edited{Explicit 4D Gaussian Splatting(Ex4DGS)}.
    Our key idea is to firstly separate static and dynamic Gaussians during training,
    and to explicitly sample positions and rotations of the dynamic Gaussians at sparse
    timestamps. The sampled positions and rotations are then interpolated to represent
    both spatially and temporally continuous motions of objects in dynamic scenes
    as well as reducing computational cost. Additionally, we introduce a progressive
    training scheme and a point-backtracking technique that improves Ex4DGS's convergence.
    We initially train Ex4DGS using short timestamps and progressively extend timestamps,
    which makes it work well with a few point clouds. The point-backtracking is used
    to quantify the cumulative error of each Gaussian over time, enabling the detection
    and removal of erroneous Gaussians in dynamic scenes. Comprehensive experiments
    on various scenes demonstrate the state-of-the-art rendering quality from our
    method, achieving fast rendering of 62 fps on a single 2080Ti GPU.
  project_page: https://leejunoh.com/Ex4DGS/
  paper: https://arxiv.org/pdf/2410.15629
  code: https://github.com/juno181/Ex4DGS
  video: null
  tags:
  - Code
  - Dynamic
  - Project
  thumbnail: assets/thumbnails/lee2024fully.jpg
  publication_date: '2024-10-21T04:25:43+00:00'
- id: hahlbohm2024efficient
  title: Efficient Perspective-Correct 3D Gaussian Splatting Using Hybrid Transparency
  authors: Florian Hahlbohm, Fabian Friederichs, Tim Weyrich, Linus Franke, Moritz
    Kappel, Susana Castillo, Marc Stamminger, Martin Eisemann, Marcus Magnor
  year: '2024'
  abstract: '3D Gaussian Splats (3DGS) have proven a versatile rendering primitive,
    both for inverse rendering as well as real-time exploration of scenes. In these
    applications, coherence across camera frames and multiple views is crucial, be
    it for robust convergence of a scene reconstruction or for artifact-free fly-throughs.
    Recent work started mitigating artifacts that break multi-view coherence, including
    popping artifacts due to inconsistent transparency sorting and perspective-correct
    outlines of (2D) splats. At the same time, real-time requirements forced such
    implementations to accept compromises in how transparency of large assemblies
    of 3D Gaussians is resolved, in turn breaking coherence in other ways. In our
    work, we aim at achieving maximum coherence, by rendering fully perspective-correct
    3D Gaussians while using a high-quality approximation of accurate blending, hybrid
    transparency, on a per-pixel level, in order to retain real-time frame rates.
    Our fast and perspectively accurate approach for evaluation of 3D Gaussians does
    not require matrix inversions, thereby ensuring numerical stability and eliminating
    the need for special handling of degenerate splats, and the hybrid transparency
    formulation for blending maintains similar quality as fully resolved per-pixel
    transparencies at a fraction of the rendering costs. We further show that each
    of these two components can be independently integrated into Gaussian splatting
    systems. In combination, they achieve up to 2$\times$ higher frame rates, 2$\times$
    faster optimization, and equal or better image quality with fewer rendering artifacts
    compared to traditional 3DGS on common benchmarks.

    '
  project_page: https://fhahlbohm.github.io/htgs/
  paper: https://arxiv.org/pdf/2410.08129.pdf
  code: null
  video: https://fhahlbohm.github.io/htgs/assets/htgs_twitter_teaser.mp4
  tags:
  - Perspective-correct
  - Project
  - Video
  thumbnail: assets/thumbnails/hahlbohm2024efficient.jpg
  publication_date: '2024-10-10T17:14:16+00:00'
  date_source: arxiv
- id: chu2024generalizable
  title: Generalizable and Animatable Gaussian Head Avatar
  authors: Xuangeng Chu, Tatsuya Harada
  year: '2024'
  abstract: In this paper, we propose Generalizable and Animatable Gaussian head Avatar
    (GAGAvatar) for one-shot animatable head avatar reconstruction. Existing methods
    rely on neural radiance fields, leading to heavy rendering consumption and low
    reenactment speeds. To address these limitations, we generate the parameters of
    3D Gaussians from a single image in a single forward pass. The key innovation
    of our work is the proposed dual-lifting method, which produces high-fidelity
    3D Gaussians that capture identity and facial details. Additionally, we leverage
    global image features and the 3D morphable model to construct 3D Gaussians for
    controlling expressions. After training, our model can reconstruct unseen identities
    without specific optimizations and perform reenactment rendering at real-time
    speeds. Experiments show that our method exhibits superior performance compared
    to previous methods in terms of reconstruction quality and expression accuracy.
    We believe our method can establish new benchmarks for future research and advance
    applications of digital avatars.
  project_page: https://xg-chu.site/project_gagavatar/
  paper: https://arxiv.org/pdf/2410.07971
  code: https://github.com/xg-chu/GAGAvatar
  video: https://youtu.be/9244ZgOl4Xk
  tags:
  - Avatar
  - Code
  - Project
  - Video
  thumbnail: assets/thumbnails/chu2024generalizable.jpg
  publication_date: '2024-10-10T14:29:00+00:00'
- id: zhu2024motiongs
  title: 'MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian
    Splatting'
  authors: Ruijie Zhu*, Yanzhe Liang*, Hanzhi Chang, Jiacheng Deng, Jiahao Lu, Wenfei
    Yang, Tianzhu Zhang, Yongdong Zhang
  year: '2024'
  abstract: Dynamic scene reconstruction is a long-term challenge in the field of
    3D vision. Recently, the emergence of 3D Gaussian Splatting has provided new insights
    into this problem. Although subsequent efforts rapidly extend static 3D Gaussian
    to dynamic scenes, they often lack explicit constraints on object motion, leading
    to optimization difficulties and performance degradation. To address the above
    issues, we propose a novel deformable 3D Gaussian splatting framework called MotionGS,
    which explores explicit motion priors to guide the deformation of 3D Gaussians.
    Specifically, we first introduce an optical flow decoupling module that decouples
    optical flow into camera flow and motion flow, corresponding to camera movement
    and object motion respectively. Then the motion flow can effectively constrain
    the deformation of 3D Gaussians, thus simulating the motion of dynamic objects.
    Additionally, a camera pose refinement module is proposed to alternately optimize
    3D Gaussians and camera poses, mitigating the impact of inaccurate camera poses.
    Extensive experiments in the monocular dynamic scenes validate that MotionGS surpasses
    state-of-the-art methods and exhibits significant superiority in both qualitative
    and quantitative results.
  project_page: https://ruijiezhu94.github.io/MotionGS_page
  paper: https://arxiv.org/pdf/2410.07707
  code: null
  video: https://youtu.be/25DgViuuKFI
  tags:
  - Dynamic
  - Project
  - Video
  thumbnail: assets/thumbnails/zhu2024motiongs.jpg
  publication_date: '2024-10-10T08:19:47+00:00'
- id: tang2024hisplat
  title: 'HiSplat: Hierarchical 3D Gaussian Splatting for Generalizable Sparse-View
    Reconstruction'
  authors: Shengji Tang, Weicai Ye, Peng Ye, Weihao Lin, Yang Zhou, Tao Chen, Wanli
    Ouyang
  year: '2024'
  abstract: 'Reconstructing 3D scenes from multiple viewpoints is a fundamental task
    in stereo vision. Recently, advances in generalizable 3D Gaussian Splatting have
    enabled high-quality novel view synthesis for unseen scenes from sparse input
    views by feed-forward predicting per-pixel Gaussian parameters without extra optimization.
    However, existing methods typically generate single-scale 3D Gaussians, which
    lack representation of both large-scale structure and texture details, resulting
    in mislocation and artefacts. In this paper, we propose a novel framework, HiSplat,
    which introduces a hierarchical manner in generalizable 3D Gaussian Splatting
    to construct hierarchical 3D Gaussians via a coarse-to-fine strategy. Specifically,
    HiSplat generates large coarse-grained Gaussians to capture large-scale structures,
    followed by fine-grained Gaussians to enhance delicate texture details. To promote
    inter-scale interactions, we propose an Error Aware Module for Gaussian compensation
    and a Modulating Fusion Module for Gaussian repair. Our method achieves joint
    optimization of hierarchical representations, allowing for novel view synthesis
    using only two-view reference images. Comprehensive experiments on various datasets
    demonstrate that HiSplat significantly enhances reconstruction quality and cross-dataset
    generalization compared to prior single-scale methods. The corresponding ablation
    study and analysis of different-scale 3D Gaussians reveal the mechanism behind
    the effectiveness. Project website: https://open3dvlab.github.io/HiSplat/

    '
  project_page: https://open3dvlab.github.io/HiSplat/
  paper: https://arxiv.org/pdf/2410.06245.pdf
  code: https://github.com/Open3DVLab/HiSplat
  video: null
  tags:
  - Code
  - Feed-Forward
  - Project
  - Sparse
  thumbnail: assets/thumbnails/tang2024hisplat.jpg
  publication_date: '2024-10-08T17:59:32+00:00'
  date_source: arxiv
- id: liu2024splatraj
  title: 'SplaTraj: Camera Trajectory Generation with Semantic Gaussian Splatting'
  authors: Xinyi Liu, Tianyi Zhang, Matthew Johnson-Roberson, Weiming Zhi
  year: '2024'
  abstract: Many recent developments for robots to represent environments have focused
    on photorealistic reconstructions. This paper particularly focuses on generating
    sequences of images from the photorealistic Gaussian Splatting models, that match
    instructions that are given by user-inputted language. We contribute a novel framework,
    SplaTraj, which formulates the generation of images within photorealistic environment
    representations as a continuous-time trajectory optimization problem. Costs are
    designed so that a camera following the trajectory poses will smoothly traverse
    through the environment and render the specified spatial information in a photogenic
    manner. This is achieved by querying a photorealistic representation with language
    embedding to isolate regions that correspond to the user-specified inputs. These
    regions are then projected to the camera's view as it moves over time and a cost
    is constructed. We can then apply gradient-based optimization and differentiate
    through the rendering to optimize the trajectory for the defined cost. The resulting
    trajectory moves to photogenically view each of the specified objects. We empirically
    evaluate our approach on a suite of environments and instructions, and demonstrate
    the quality of generated image sequences.
  project_page: null
  paper: https://arxiv.org/pdf/2410.06014
  code: null
  video: https://youtu.be/PUXNBfpeZkg
  tags:
  - Language Embedding
  - Rendering
  - Video
  thumbnail: assets/thumbnails/liu2024splatraj.jpg
  publication_date: '2024-10-08T13:16:49+00:00'
- id: zhang2024monst3r
  title: 'MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion'
  authors: Junyi Zhang, Charles Herrmann, Junhwa Hur, Varun Jampani, Trevor Darrell,
    Forrester Cole, Deqing Sun, Ming-Hsuan Yang
  year: '2024'
  abstract: 'Estimating geometry from dynamic scenes, where objects move and deform
    over time, remains a core challenge in computer vision. Current approaches often
    rely on multi-stage pipelines or global optimizations that decompose the problem
    into subtasks, like depth and flow, leading to complex systems prone to errors.
    In this paper, we present Motion DUSt3R (MonST3R), a novel geometry-first approach
    that directly estimates per-timestep geometry from dynamic scenes. Our key insight
    is that by simply estimating a pointmap for each timestep, we can effectively
    adapt DUST3R''s representation, previously only used for static scenes, to dynamic
    scenes. However, this approach presents a significant challenge: the scarcity
    of suitable training data, namely dynamic, posed videos with depth labels. Despite
    this, we show that by posing the problem as a fine-tuning task, identifying several
    suitable datasets, and strategically training the model on this limited data,
    we can surprisingly enable the model to handle dynamics, even without an explicit
    motion representation. Based on this, we introduce new optimizations for several
    downstream video-specific tasks and demonstrate strong performance on video depth
    and camera pose estimation, outperforming prior work in terms of robustness and
    efficiency. Moreover, MonST3R shows promising results for primarily feed-forward
    4D reconstruction.

    '
  project_page: https://monst3r-project.github.io/
  paper: https://arxiv.org/pdf/2410.03825.pdf
  code: https://github.com/Junyi42/monst3r
  video: https://monst3r-project.github.io/files/teaser_vid_v2_lowres.mp4
  tags:
  - 3ster-based
  - Code
  - Dynamic
  - Project
  - Sparse
  - Video
  thumbnail: assets/thumbnails/zhang2024monst3r.jpg
  publication_date: '2024-10-04T18:00:07+00:00'
  date_source: arxiv
- id: chen2024gigs
  title: 'GI-GS: Global Illumination Decomposition on Gaussian Splatting for Inverse
    Rendering'
  authors: Hongze Chen, Zehong Lin, Jun Zhang
  year: '2024'
  abstract: 'We present GI-GS, a novel inverse rendering framework that leverages
    3D Gaussian Splatting (3DGS) and deferred shading to achieve photo-realistic novel
    view synthesis and relighting. In inverse rendering, accurately modeling the shading
    processes of objects is essential for achieving high-fidelity results. Therefore,
    it is critical to incorporate global illumination to account for indirect lighting
    that reaches an object after multiple bounces across the scene. Previous 3DGS-based
    methods have attempted to model indirect lighting by characterizing indirect illumination
    as learnable lighting volumes or additional attributes of each Gaussian, while
    using baked occlusion to represent shadow effects. These methods, however, fail
    to accurately model the complex physical interactions between light and objects,
    making it impossible to construct realistic indirect illumination during relighting.
    To address this limitation, we propose to calculate indirect lighting using efficient
    path tracing with deferred shading. In our framework, we first render a G-buffer
    to capture the detailed geometry and material properties of the scene. Then, we
    perform physically-based rendering (PBR) only for direct lighting. With the G-buffer
    and previous rendering results, the indirect lighting can be calculated through
    a lightweight path tracing. Our method effectively models indirect lighting under
    any given lighting conditions, thereby achieving better novel view synthesis and
    relighting. Quantitative and qualitative results show that our GI-GS outperforms
    existing baselines in both rendering quality and efficiency.

    '
  project_page: https://stopaimme.github.io/GI-GS/
  paper: https://arxiv.org/pdf/2410.02619.pdf
  code: https://github.com/stopaimme/GI-GS
  video: null
  tags:
  - Code
  - Project
  - Ray Tracing
  - Relight
  thumbnail: assets/thumbnails/chen2024gigs.jpg
  publication_date: '2024-10-03T15:58:18+00:00'
  date_source: arxiv
- id: xie2024supergs
  title: 'SuperGS: Super-Resolution 3D Gaussian Splatting via Latent Feature Field
    and Gradient-guided Splitting'
  authors: Shiyun Xie, Zhiru Wang, Yinghao Zhu, Chengwei Pan
  year: '2024'
  abstract: 'Recently, 3D Gaussian Splatting (3DGS) has exceled in novel view synthesis
    with its real-time rendering capabilities and superior quality. However, it faces
    challenges for high-resolution novel view synthesis (HRNVS) due to the coarse
    nature of primitives derived from low-resolution input views. To address this
    issue, we propose Super-Resolution 3DGS (SuperGS), which is an expansion of 3DGS
    designed with a two-stage coarse-to-fine training framework, utilizing pretrained
    low-resolution scene representation as an initialization for super-resolution
    optimization. Moreover, we introduce Multi-resolution Feature Gaussian Splatting
    (MFGS) to incorporates a latent feature field for flexible feature sampling and
    Gradient-guided Selective Splitting (GSS) for effective Gaussian upsampling. By
    integrating these strategies within the coarse-to-fine framework ensure both high
    fidelity and memory efficiency. Extensive experiments demonstrate that SuperGS
    surpasses state-of-the-art HRNVS methods on challenging real-world datasets using
    only low-resolution inputs.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2410.02571v1.pdf
  code: null
  video: null
  tags:
  - Densification
  - Feed-Forward
  - Rendering
  thumbnail: assets/thumbnails/xie2024supergs.jpg
  publication_date: '2024-10-03T15:18:28+00:00'
  date_source: arxiv
- id: cao20243dgsdet
  title: '3DGS-DET: Empower 3D Gaussian Splatting with Boundary Guidance and Box-Focused
    Sampling for 3D Object Detection'
  authors: Yang Cao, Yuanliang Jv, Dan Xu
  year: '2024'
  abstract: 'Neural Radiance Fields (NeRF) are widely used for novel-view synthesis
    and have been adapted for 3D Object Detection (3DOD), offering a promising approach
    to 3D object detection through view-synthesis representation. However, NeRF faces
    inherent limitations: (i) It has limited representational capacity for 3DOD due
    to its implicit nature, and (ii) it suffers from slow rendering speeds. Recently,
    3D Gaussian Splatting (3DGS) has emerged as an explicit 3D representation that
    addresses these limitations with faster rendering capabilities. Inspired by these
    advantages, this paper introduces 3DGS into 3DOD for the first time, identifying
    two main challenges: (i) Ambiguous spatial distribution of Gaussian blobs – 3DGS
    primarily relies on 2D pixel-level supervision, resulting in unclear 3D spatial
    distribution of Gaussian blobs and poor differentiation between objects and background,
    which hinders 3DOD; (ii) Excessive background blobs – 2D images often include
    numerous background pixels, leading to densely reconstructed 3DGS with many noisy
    Gaussian blobs representing the background, negatively affecting detection. To
    tackle the challenge (i), we leverage the fact that 3DGS reconstruction is derived
    from 2D images, and propose an elegant and efficient solution by incorporating
    2D Boundary Guidance to significantly enhance the spatial distribution of Gaussian
    blobs, resulting in clearer differentiation between objects and their background
    (see Fig. 1). To address the challenge (ii), we propose a Box-Focused Sampling
    strategy using 2D boxes to generate object probability distribution in 3D spaces,
    allowing effective probabilistic sampling in 3D to retain more object blobs and
    reduce noisy background blobs. Benefiting from the proposed Boundary Guidance
    and Box-Focused Sampling, our final method, 3DGS-DET, achieves significant improvements
    (+5.6 on mAP@0.25, +3.7 on mAP@0.5) over our basic pipeline version, without introducing
    any additional learnable parameters. Furthermore, 3DGS-DET significantly outperforms
    the state-of-the-art NeRF-based method, NeRF-Det, achieving improvements of +6.6
    on mAP@0.25 and +8.1 on mAP@0.5 for the ScanNet dataset, and impressive +31.5
    on mAP@0.25 for the ARKITScenes dataset. Codes and models are publicly available
    at: https://github.com/yangcaoai/3DGS-DET.'
  project_page: null
  paper: https://arxiv.org/pdf/2410.01647
  code: null
  video: null
  tags:
  - Object Detection
  thumbnail: assets/thumbnails/cao20243dgsdet.jpg
  publication_date: '2024-10-02T15:15:52+00:00'
- id: duisterhof2024mast3rsfm
  title: 'MASt3R-SfM: a Fully-Integrated Solution for Unconstrained Structure-from-Motion'
  authors: Bardienus Duisterhof, Lojze Zust, Philippe Weinzaepfel, Vincent Leroy,
    Yohann Cabon, Jerome Revaud
  year: '2024'
  abstract: 'Structure-from-Motion (SfM), a task aiming at jointly recovering camera
    poses and 3D geometry of a scene given a set of images, remains a hard problem
    with still many open challenges despite decades of significant progress. The traditional
    solution for SfM consists of a complex pipeline of minimal solvers which tends
    to propagate errors and fails when images do not sufficiently overlap, have too
    little motion, etc. Recent methods have attempted to revisit this paradigm, but
    we empirically show that they fall short of fixing these core issues. In this
    paper, we propose instead to build upon a recently released foundation model for
    3D vision that can robustly produce local 3D reconstructions and accurate matches.
    We introduce a low-memory approach to accurately align these local reconstructions
    in a global coordinate system. We further show that such foundation models can
    serve as efficient image retrievers without any overhead, reducing the overall
    complexity from quadratic to linear. Overall, our novel SfM pipeline is simple,
    scalable, fast and truly unconstrained, i.e. it can handle any collection of images,
    ordered or not. Extensive experiments on multiple benchmarks show that our method
    provides steady performance across diverse settings, especially outperforming
    existing methods in small- and medium-scale settings.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2409.19152.pdf
  code: https://github.com/naver/mast3r
  video: null
  tags:
  - Code
  thumbnail: assets/thumbnails/duisterhof2024mast3rsfm.jpg
  publication_date: '2024-09-27T21:29:58+00:00'
  date_source: arxiv
- id: yu2024languageembedded
  title: 'Language-Embedded Gaussian Splats (LEGS): Incrementally Building Room-Scale
    Representations with a Mobile Robot'
  authors: Justin Yu, Kush Hari, Kishore Srinivas, Karim El-Refai, Adam Rashid, Chung
    Min Kim, Justin Kerr, Richard Cheng, Muhammad Zubair Irshad, Ashwin Balakrishna,
    Thomas Kollar, Ken Goldberg
  year: '2024'
  abstract: 'Building semantic 3D maps is valuable for searching for objects of interest
    in offices, warehouses, stores, and homes. We present a mapping system that incrementally
    builds a Language-Embedded Gaussian Splat (LEGS): a detailed 3D scene representation
    that encodes both appearance and semantics in a unified representation. LEGS is
    trained online as a robot traverses its environment to enable localization of
    open-vocabulary object queries. We evaluate LEGS on 4 room-scale scenes where
    we query for objects in the scene to assess how LEGS can capture semantic meaning.
    We compare LEGS to LERF and find that while both systems have comparable object
    query success rates, LEGS trains over 3.5x faster than LERF. Results suggest that
    a multi-camera setup and incremental bundle adjustment can boost visual reconstruction
    quality in constrained robot trajectories, and suggest LEGS can localize open-vocabulary
    and long-tail object queries with up to 66% accuracy.'
  project_page: https://berkeleyautomation.github.io/LEGS/
  paper: https://arxiv.org/pdf/2409.18108
  code: https://github.com/BerkeleyAutomation/L3GS
  video: null
  tags:
  - Code
  - Language Embedding
  - Project
  - Robotics
  - Segmentation
  thumbnail: assets/thumbnails/yu2024languageembedded.jpg
  publication_date: '2024-09-26T17:51:31+00:00'
- id: wang2024v3
  title: 'V^3: Viewing Volumetric Videos on Mobiles via Streamable 2D Dynamic Gaussians'
  authors: Penghao Wang, Zhirui Zhang, Liao Wang, Kaixin Yao, Siyuan Xie, Jingyi Yu,
    Minye Wu, Lan Xu
  year: '2024'
  abstract: Experiencing high-fidelity volumetric video as seamlessly as 2D videos
    is a long-held dream. However, current dynamic 3DGS methods, despite their high
    rendering quality, face challenges in streaming on mobile devices due to computational
    and bandwidth constraints. In this paper, we introduce V^3 (Viewing Volumetric
    Videos), a novel approach that enables high-quality mobile rendering through the
    streaming of dynamic Gaussians. Our key innovation is to view dynamic 3DGS as
    2D videos, facilitating the use of hardware video codecs. Additionally, we propose
    a two-stage training strategy to reduce storage requirements with rapid training
    speed. The first stage employs hash encoding and shallow MLP to learn motion,
    then reduces the number of Gaussians through pruning to meet the streaming requirements,
    while the second stage fine tunes other Gaussian attributes using residual entropy
    loss and temporal loss to improve temporal continuity. This strategy, which disentangles
    motion and appearance, maintains high rendering quality with compact storage requirements.
    Meanwhile, we designed a multi-platform player to decode and render 2D Gaussian
    videos. Extensive experiments demonstrate the effectiveness of V^3, outperforming
    other methods by enabling high-quality rendering and streaming on common devices,
    which is unseen before. As the first to stream dynamic Gaussians on mobile devices,
    our companion player offers users an unprecedented volumetric video experience,
    including smooth scrolling and instant sharing. Our project page with source code
    is available at this https URL.
  project_page: https://authoritywang.github.io/v3/
  paper: https://arxiv.org/pdf/2409.13648
  code: https://github.com/AuthorityWang/VideoGS
  video: https://youtu.be/Z5La9AporRU?si=iJ-m_mvUSxQN4Bwm
  tags:
  - Avatar
  - Code
  - Project
  - Video
  thumbnail: assets/thumbnails/wang2024v3.jpg
  publication_date: '2024-09-20T16:54:27+00:00'
- id: chelani2024edgegaussians
  title: EdgeGaussians -- 3D Edge Mapping via Gaussian Splatting
  authors: Kunal Chelani, Assia Benbihi, Torsten Sattler, Fredrik Kahl
  year: '2024'
  abstract: 'With their meaningful geometry and their omnipresence in the 3D world,
    edges are extremely useful primitives in computer vision. 3D edges comprise of
    lines and curves, and methods to reconstruct them use either multi-view images
    or point clouds as input. State-of-the-art image-based methods first learn a 3D
    edge point cloud then fit 3D edges to it. The edge point cloud is obtained by
    learning a 3D neural implicit edge field from which the 3D edge points are sampled
    on a specific level set (0 or 1). However, such methods present two important
    drawbacks: i) it is not realistic to sample points on exact level sets due to
    float imprecision and training inaccuracies. Instead, they are sampled within
    a range of levels so the points do not lie accurately on the 3D edges and require
    further processing. ii) Such implicit representations are computationally expensive
    and require long training times. In this paper, we address these two limitations
    and propose a 3D edge mapping that is simpler, more efficient, and preserves accuracy.
    Our method learns explicitly the 3D edge points and their edge direction hence
    bypassing the need for point sampling. It casts a 3D edge point as the center
    of a 3D Gaussian and the edge direction as the principal axis of the Gaussian.
    Such a representation has the advantage of being not only geometrically meaningful
    but also compatible with the efficient training optimization defined in Gaussian
    Splatting. Results show that the proposed method produces edges as accurate and
    complete as the state-of-the-art while being an order of magnitude faster.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2409.12886.pdf
  code: https://github.com/kunalchelani/EdgeGaussians
  video: null
  tags:
  - Code
  - Rendering
  thumbnail: assets/thumbnails/chelani2024edgegaussians.jpg
  publication_date: '2024-09-19T16:28:45+00:00'
  date_source: arxiv
- id: joseph2024gradientdriven
  title: Gradient-Driven 3D Segmentation and Affordance Transfer in Gaussian Splatting
    Using 2D Masks
  authors: Joji Joseph, Bharadwaj Amrutur, Shalabh Bhatnagar
  year: '2024'
  abstract: In this paper, we introduce a novel voting-based method that extends 2D
    segmentation models to 3D Gaussian splats. Our approach leverages masked gradients,
    where gradients are filtered by input 2D masks, and these gradients are used as
    votes to achieve accurate segmentation. As a byproduct, we found that inference-time
    gradients can also be used to prune Gaussians, resulting in up to 21% compression.
    Additionally, we explore few-shot affordance transfer, allowing annotations from
    2D images to be effectively transferred onto 3D Gaussian splats. The robust yet
    straightforward mathematical formulation underlying this approach makes it a highly
    effective tool for numerous downstream applications, such as augmented reality
    (AR), object editing, and robotics.
  project_page: https://jojijoseph.github.io/3dgs-segmentation/
  paper: https://arxiv.org/pdf/2409.11681
  code: https://github.com/JojiJoseph/3dgs-gradient-segmentation
  video: null
  tags:
  - Code
  - Project
  - Segmentation
  thumbnail: assets/thumbnails/joseph2024gradientdriven.jpg
  publication_date: '2024-09-18T03:45:44+00:00'
- id: mihajlovic2024splatfields
  title: 'SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction'
  authors: Marko Mihajlovic, Sergey Prokudin, Siyu Tang, Robert Maier, Federica Bogo,
    Tony Tung, Edmond Boyer
  year: '2024'
  abstract: Digitizing 3D static scenes and 4D dynamic events from multi-view images
    has long been a challenge in computer vision and graphics. Recently, 3D Gaussian
    Splatting (3DGS) has emerged as a practical and scalable reconstruction method,
    gaining popularity due to its impressive reconstruction quality, real-time rendering
    capabilities, and compatibility with widely used visualization tools. However,
    the method requires a substantial number of input views to achieve high-quality
    scene reconstruction, introducing a significant practical bottleneck. This challenge
    is especially severe in capturing dynamic scenes, where deploying an extensive
    camera array can be prohibitively costly. In this work, we identify the lack of
    spatial autocorrelation of splat features as one of the factors contributing to
    the suboptimal performance of the 3DGS technique in sparse reconstruction settings.
    To address the issue, we propose an optimization strategy that effectively regularizes
    splat features by modeling them as the outputs of a corresponding implicit neural
    field. This results in a consistent enhancement of reconstruction quality across
    various scenarios. Our approach effectively handles static and dynamic cases,
    as demonstrated by extensive testing across different setups and scene complexities.
  project_page: https://markomih.github.io/SplatFields/
  paper: https://arxiv.org/pdf/2409.11211.pdf
  code: https://github.com/markomih/SplatFields
  video: null
  tags:
  - Code
  - Project
  - Sparse
  thumbnail: assets/thumbnails/mihajlovic2024splatfields.jpg
  publication_date: '2024-09-17T14:04:20+00:00'
- id: meng2024beings
  title: 'BEINGS: Bayesian Embodied Image-goal Navigation with Gaussian Splatting'
  authors: Wugang Meng, Tianfu Wu, Huan Yin, Fumin Zhang
  year: '2024'
  abstract: Image-goal navigation enables a robot to reach the location where a target
    image was captured, using visual cues for guidance. However, current methods either
    rely heavily on data and computationally expensive learning-based approaches or
    lack efficiency in complex environments due to insufficient exploration strategies.
    To address these limitations, we propose Bayesian Embodied Image-goal Navigation
    Using Gaussian Splatting, a novel method that formulates ImageNav as an optimal
    control problem within a model predictive control framework. BEINGS leverages
    3D Gaussian Splatting as a scene prior to predict future observations, enabling
    efficient, real-time navigation decisions grounded in the robot’s sensory experiences.
    By integrating Bayesian updates, our method dynamically refines the robot's strategy
    without requiring extensive prior experience or data. Our algorithm is validated
    through extensive simulations and physical experiments, showcasing its potential
    for embodied robot systems in visually complex scenarios.
  project_page: https://www.mwg.ink/BEINGS-web
  paper: https://arxiv.org/pdf/2409.10216
  code: https://github.com/guaMass/BEINGS
  video: null
  tags:
  - Autonomous Driving
  - Code
  - Project
  - Robotics
  thumbnail: assets/thumbnails/meng2024beings.jpg
  publication_date: '2024-09-16T12:07:02+00:00'
- id: li2024pshuman
  title: 'PSHuman: Photorealistic Single-view Human Reconstruction using Cross-Scale
    Diffusion'
  authors: Peng Li, Wangguandong Zheng, Yuan Liu, Tao Yu, Yangguang Li, Xingqun Qi,
    Mengfei Li, Xiaowei Chi, Siyu Xia, Wei Xue, Wenhan Luo, Qifeng Liu, Yike Guo
  year: '2024'
  abstract: 'Detailed and photorealistic 3D human modeling is essential for various
    applications and has seen tremendous progress. However, full-body reconstruction
    from a monocular RGB image remains challenging due to the ill-posed nature of
    the problem and sophisticated clothing topology with self-occlusions. In this
    paper, we propose PSHuman, a novel framework that explicitly reconstructs human
    meshes utilizing priors from the multiview diffusion model. It is found that directly
    applying multiview diffusion on single-view human images leads to severe geometric
    distortions, especially on generated faces. To address it, we propose a cross-scale
    diffusion that models the joint probability distribution of global full-body shape
    and local facial characteristics, enabling detailed and identity-preserved novel-view
    generation without any geometric distortion. Moreover, to enhance cross-view body
    shape consistency of varied human poses, we condition the generative model on
    parametric models like SMPL-X, which provide body priors and prevent unnatural
    views inconsistent with human anatomy. Leveraging the generated multi-view normal
    and color images, we present SMPLX-initialized explicit human carving to recover
    realistic textured human meshes efficiently. Extensive experimental results and
    quantitative evaluations on CAPE and THuman2.1 datasets demonstrate PSHumans superiority
    in geometry details, texture fidelity, and generalization capability.

    '
  project_page: https://penghtyx.github.io/PSHuman/
  paper: https://arxiv.org/pdf/2409.10141.pdf
  code: https://github.com/pengHTYX/PSHuman
  video: null
  tags:
  - Avatar
  - Code
  - Diffusion
  - Meshing
  - Project
  thumbnail: assets/thumbnails/li2024pshuman.jpg
  publication_date: '2024-09-16T10:13:06+00:00'
  date_source: arxiv
- id: jiang2024dualgs
  title: 'DualGS: Robust Dual Gaussian Splatting for Immersive Human-centric Volumetric
    Videos'
  authors: Yuheng Jiang, Zhehao Shen, Yu Hong, Chengcheng Guo, Yize Wu, Yingliang
    Zhang, Jingyi Yu, Lan Xu
  year: '2024'
  abstract: Volumetric video represents a transformative advancement in visual media,
    enabling users to freely navigate immersive virtual experiences and narrowing
    the gap between digital and real worlds. However, the need for extensive manual
    intervention to stabilize mesh sequences and the generation of excessively large
    assets in existing workflows impedes broader adoption. In this paper, we present
    a novel Gaussian-based approach, dubbed \textit{DualGS}, for real-time and high-fidelity
    playback of complex human performance with excellent compression ratios. Our key
    idea in DualGS is to separately represent motion and appearance using the corresponding
    skin and joint Gaussians. Such an explicit disentanglement can significantly reduce
    motion redundancy and enhance temporal coherence. We begin by initializing the
    DualGS and anchoring skin Gaussians to joint Gaussians at the first frame. Subsequently,
    we employ a coarse-to-fine training strategy for frame-by-frame human performance
    modeling. It includes a coarse alignment phase for overall motion prediction as
    well as a fine-grained optimization for robust tracking and high-fidelity rendering.
    To integrate volumetric video seamlessly into VR environments, we efficiently
    compress motion using entropy encoding and appearance using codec compression
    coupled with a persistent codebook. Our approach achieves a compression ratio
    of up to 120 times, only requiring approximately 350KB of storage per frame. We
    demonstrate the efficacy of our representation through photo-realistic, free-view
    experiences on VR headsets, enabling users to immersively watch musicians in performance
    and feel the rhythm of the notes at the performers' fingertips.
  project_page: https://nowheretrix.github.io/DualGS/
  paper: https://arxiv.org/pdf/2409.08353
  code: null
  video: https://www.youtube.com/watch?v=vwDE8xr78Bg
  tags:
  - Avatar
  - Project
  - Video
  thumbnail: assets/thumbnails/jiang2024dualgs.jpg
  publication_date: '2024-09-12T18:33:13+00:00'
- id: liao2024fisheyegs
  title: 'Fisheye-GS: Lightweight and Extensible Gaussian Splatting Module for Fisheye
    Cameras'
  authors: Zimu Liao, Siyan Chen, Rong Fu, Yi Wang, Zhongling Su, Hao Luo, Li Ma,
    Linning Xu, Bo Dai, Hengjie Li, Zhilin Pei, Xingcheng Zhang
  year: '2024'
  abstract: 'Recently, 3D Gaussian Splatting (3DGS) has garnered attention for its
    high fidelity and real-time rendering. However, adapting 3DGS to different camera
    models, particularly fisheye lenses, poses challenges due to the unique 3D to
    2D projection calculation. Additionally, there are inefficiencies in the tile-based
    splatting, especially for the extreme curvature and wide field of view of fisheye
    lenses, which are crucial for its broader real-life applications. To tackle these
    challenges, we introduce Fisheye-GS.This innovative method recalculates the projection
    transformation and its gradients for fisheye cameras. Our approach can be seamlessly
    integrated as a module into other efficient 3D rendering methods, emphasizing
    its extensibility, lightweight nature, and modular design. Since we only modified
    the projection component, it can also be easily adapted for use with different
    camera models. Compared to methods that train after undistortion, our approach
    demonstrates a clear improvement in visual quality.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2409.04751.pdf
  code: https://github.com/zmliao/Fisheye-GS
  video: null
  tags:
  - Code
  - Rendering
  thumbnail: assets/thumbnails/liao2024fisheyegs.jpg
  publication_date: '2024-09-07T07:53:40+00:00'
  date_source: arxiv
- id: seidenschwarz2024dynomo
  title: 'DynOMo: Online Point Tracking by Dynamic Online Monocular Gaussian Reconstruction'
  authors: Jenny Seidenschwarz, Qunjie Zhou, Bardienus Duisterhof, Deva Ramanan, Laura
    Leal-Taixé
  year: '2024'
  abstract: 'Reconstructing scenes and tracking motion are two sides of the same coin.
    Tracking points allow for geometric reconstruction [14], while geometric reconstruction
    of (dynamic) scenes allows for 3D tracking of points over time [24, 39]. The latter
    was recently also exploited for 2D point tracking to overcome occlusion ambiguities
    by lifting tracking directly into 3D [38]. However, above approaches either require
    offline processing or multi-view camera setups both unrealistic for real-world
    applications like robot navigation or mixed reality. We target the challenge of
    online 2D and 3D point tracking from unposed monocular camera input introducing
    Dynamic Online Monocular Reconstruction (DynOMo). We leverage 3D Gaussian splatting
    to reconstruct dynamic scenes in an online fashion. Our approach extends 3D Gaussians
    to capture new content and object motions while estimating camera movements from
    a single RGB frame. DynOMo stands out by enabling emergence of point trajectories
    through robust image feature reconstruction and a novel similarity-enhanced regularization
    term, without requiring any correspondence-level supervision. It sets the first
    baseline for online point tracking with monocular unposed cameras, achieving performance
    on par with existing methods. We aim to inspire the community to advance online
    point tracking and reconstruction, expanding the applicability to diverse real-world
    scenarios.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2409.02104.pdf
  code: null
  video: null
  tags:
  - Dynamic
  - Monocular
  thumbnail: assets/thumbnails/seidenschwarz2024dynomo.jpg
  publication_date: '2024-09-03T17:58:03+00:00'
  date_source: arxiv
- id: chen2024omnire
  title: 'OmniRe: Omni Urban Scene Reconstruction'
  authors: Ziyu Chen, Jiawei Yang, Jiahui Huang, Riccardo de Lutio, Janick Martinez
    Esturo, Boris Ivanovic, Or Litany, Zan Gojcic, Sanja Fidler, Marco Pavone, Li
    Song, Yue Wang
  year: '2024'
  abstract: We introduce OmniRe, a holistic approach for efficiently reconstructing
    high-fidelity dynamic urban scenes from on-device logs. Recent methods for modeling
    driving sequences using neural radiance fields or Gaussian Splatting have demonstrated
    the potential of reconstructing challenging dynamic scenes, but often overlook
    pedestrians and other non-vehicle dynamic actors, hindering a complete pipeline
    for dynamic urban scene reconstruction. To that end, we propose a comprehensive
    3DGS framework for driving scenes, named OmniRe, that allows for accurate, full-length
    reconstruction of diverse dynamic objects in a driving log. OmniRe builds dynamic
    neural scene graphs based on Gaussian representations and constructs multiple
    local canonical spaces that model various dynamic actors, including vehicles,
    pedestrians, and cyclists, among many others. This capability is unmatched by
    existing methods. OmniRe allows us to holistically reconstruct different objects
    present in the scene, subsequently enabling the simulation of reconstructed scenarios
    with all actors participating in real-time (~60Hz). Extensive evaluations on the
    Waymo dataset show that our approach outperforms prior state-of-the-art methods
    quantitatively and qualitatively by a large margin. We believe our work fills
    a critical gap in driving reconstruction.
  project_page: https://ziyc.github.io/omnire/
  paper: https://arxiv.org/pdf/2408.16760
  code: https://github.com/ziyc/drivestudio
  video: null
  tags:
  - Autonomous Driving
  - Code
  - Project
  thumbnail: assets/thumbnails/chen2024omnire.jpg
  publication_date: '2024-08-29T17:56:33+00:00'
- id: wang20243d
  title: 3D Reconstruction with Spatial Memory
  authors: Hengyi Wang, Lourdes Agapito
  year: '2024'
  abstract: 'We present Spann3R, a novel approach for dense 3D reconstruction from
    ordered or unordered image collections. Built on the DUSt3R paradigm, Spann3R
    uses a transformer-based architecture to directly regress pointmaps from images
    without any prior knowledge of the scene or camera parameters. Unlike DUSt3R,
    which predicts per image-pair pointmaps each expressed in its local coordinate
    frame, Spann3R can predict per-image pointmaps expressed in a global coordinate
    system, thus eliminating the need for optimization-based global alignment. The
    key idea of Spann3R is to manage an external spatial memory that learns to keep
    track of all previous relevant 3D information. Spann3R then queries this spatial
    memory to predict the 3D structure of the next frame in a global coordinate system.
    Taking advantage of DUSt3R''s pre-trained weights, and further fine-tuning on
    a subset of datasets, Spann3R shows competitive performance and generalization
    ability on various unseen datasets and can process ordered image collections in
    real time. Project page: \url{https://hengyiwang.github.io/projects/spanner}

    '
  project_page: https://hengyiwang.github.io/projects/spanner
  paper: https://arxiv.org/pdf/2408.16061.pdf
  code: https://github.com/HengyiWang/spann3r
  video: https://hengyiwang.github.io/projects/spanner/videos/spanner_intro.mp4
  tags:
  - 3ster-based
  - Code
  - Project
  - SLAM
  - Video
  thumbnail: assets/thumbnails/wang20243d.jpg
  publication_date: '2024-08-28T18:01:00+00:00'
  date_source: arxiv
- id: shi2024lapisgs
  title: 'LapisGS: Layered Progressive 3D Gaussian Splatting for Adaptive Streaming'
  authors: Yuang Shi, Simone Gasparini, Géraldine Morin, Wei Tsang Ooi,
  year: '2024'
  abstract: The rise of Extended Reality (XR) requires efficient streaming of 3D online
    worlds, challenging current 3DGS representations to adapt to bandwidth-constrained
    environments. We propose LapisGS, a layered 3DGS that supports adaptive streaming
    and progressive rendering. Our method constructs a layered structure for cumulative
    representation, incorporates dynamic opacity optimization to maintain visual fidelity,
    and utilizes occupancy maps to efficiently manage Gaussian splats. This proposed
    model offers a progressive representation supporting a continuous rendering quality
    adapted for bandwidth-aware streaming. Extensive experiments validate the effectiveness
    of our approach in balancing visual fidelity with the compactness of the model,
    with up to 50.71% improvement in SSIM, 286.53% improvement in LPIPS, and 318.41%
    reduction in model size, and shows its potential for bandwidth-adapted 3D streaming
    and rendering applications.
  project_page: https://yuang-ian.github.io/lapisgs/
  paper: https://arxiv.org/pdf/2408.14823
  code: null
  video: null
  tags:
  - Compression
  - Project
  thumbnail: assets/thumbnails/shi2024lapisgs.jpg
  publication_date: '2024-08-27T07:06:49+00:00'
- id: smart2024splatt3r
  title: 'Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs'
  authors: Brandon Smart, Chuanxia Zheng, Iro Laina, Victor Adrian Prisacariu
  year: '2024'
  abstract: 'In this paper, we introduce Splatt3R, a pose-free, feed-forward method
    for in-the-wild 3D reconstruction and novel view synthesis from stereo pairs.
    Given uncalibrated natural images, Splatt3R can predict 3D Gaussian Splats without
    requiring any camera parameters or depth information. For generalizability, we
    build Splatt3R upon a ``foundation'''' 3D geometry reconstruction method, MASt3R,
    by extending it to deal with both 3D structure and appearance. Specifically, unlike
    the original MASt3R which reconstructs only 3D point clouds, we predict the additional
    Gaussian attributes required to construct a Gaussian primitive for each point.
    Hence, unlike other novel view synthesis methods, Splatt3R is first trained by
    optimizing the 3D point cloud''s geometry loss, and then a novel view synthesis
    objective. By doing this, we avoid the local minima present in training 3D Gaussian
    Splats from stereo views. We also propose a novel loss masking strategy that we
    empirically find is critical for strong performance on extrapolated viewpoints.
    We train Splatt3R on the ScanNet++ dataset and demonstrate excellent generalisation
    to uncalibrated, in-the-wild images. Splatt3R can reconstruct scenes at 4FPS at
    512 x 512 resolution, and the resultant splats can be rendered in real-time.

    '
  project_page: https://splatt3r.active.vision/
  paper: https://arxiv.org/pdf/2408.13912.pdf
  code: https://github.com/btsmart/splatt3r
  video: null
  tags:
  - 3ster-based
  - Code
  - Project
  - Rendering
  thumbnail: assets/thumbnails/smart2024splatt3r.jpg
  publication_date: '2024-08-25T18:27:20+00:00'
  date_source: arxiv
- id: zhang202425
  title: 'TranSplat: Generalizable 3D Gaussian Splatting from Sparse Multi-View Images
    with Transformers'
  authors: Chuanrui Zhang, Yingshuang Zou, Zhuoling Li, Minmin Yi, Haoqian Wang
  year: '2024'
  abstract: Compared with previous 3D reconstruction methods like Nerf, recent Generalizable
    3D Gaussian Splatting (G-3DGS) methods demonstrate impressive efficiency even
    in the sparse-view setting. However, the promising reconstruction performance
    of existing G-3DGS methods relies heavily on accurate multi-view feature matching,
    which is quite challenging. Especially for the scenes that have many non-overlapping
    areas between various views and contain numerous similar regions, the matching
    performance of existing methods is poor and the reconstruction precision is limited.
    To address this problem, we develop a strategy that utilizes a predicted depth
    confidence map to guide accurate local feature matching. In addition, we propose
    to utilize the knowledge of existing monocular depth estimation models as prior
    to boost the depth estimation precision in non-overlapping areas between views.
    Combining the proposed strategies, we present a novel G-3DGS method named TranSplat,
    which obtains the best performance on both the RealEstate10K and ACID benchmarks
    while maintaining competitive speed and presenting strong cross-dataset generalization
    ability.
  project_page: https://xingyoujun.github.io/transplat/
  paper: https://arxiv.org/pdf/2408.13770
  code: https://github.com/xingyoujun/transplat
  video: null
  tags:
  - Code
  - Feed-Forward
  - Project
  - Sparse
  - Transformer
  thumbnail: assets/thumbnails/zhang202425.jpg
  publication_date: '2024-08-25T08:37:57+00:00'
- id: dihlmann2024subsurface
  title: Subsurface Scattering for 3D Gaussian Splatting
  authors: Jan-Niklas Dihlmann, Arjun Majumdar, Andreas Engelhardt, Raphael Braun,
    Hendrik P. A. Lensch
  year: '2024'
  abstract: '3D reconstruction and relighting of objects made from scattering materials
    present a significant challenge due to the complex light transport beneath the
    surface. 3D Gaussian Splatting introduced high-quality novel view synthesis at
    real-time speeds. While 3D Gaussians efficiently approximate an object''s surface,
    they fail to capture the volumetric properties of subsurface scattering. We propose
    a framework for optimizing an object''s shape together with the radiance transfer
    field given multi-view OLAT (one light at a time) data. Our method decomposes
    the scene into an explicit surface represented as 3D Gaussians, with a spatially
    varying BRDF, and an implicit volumetric representation of the scattering component.
    A learned incident light field accounts for shadowing. We optimize all parameters
    jointly via ray-traced differentiable rendering. Our approach enables material
    editing, relighting and novel view synthesis at interactive rates. We show successful
    application on synthetic data and introduce a newly acquired multi-view multi-light
    dataset of objects in a light-stage setup. Compared to previous work we achieve
    comparable or better results at a fraction of optimization and rendering time
    while enabling detailed control over material attributes. Project page https://sss.jdihlmann.com/

    '
  project_page: https://sss.jdihlmann.com/
  paper: https://arxiv.org/pdf/2408.12282.pdf
  code: null
  video: null
  tags:
  - Project
  - Relight
  - Rendering
  thumbnail: assets/thumbnails/dihlmann2024subsurface.jpg
  publication_date: '2024-08-22T10:34:01+00:00'
  date_source: arxiv
- id: liu2024gsloc
  title: 'GSLoc: Efficient Camera Pose Refinement via 3D Gaussian Splatting'
  authors: Changkun Liu, Shuai Chen, Yash Bhalgat, Siyan Hu, Ming Cheng, Zirui Wang,
    Victor Adrian Prisacariu, Tristan Braud
  year: '2024'
  abstract: We leverage 3D Gaussian Splatting (3DGS) as a scene representation and
    propose a novel test-time camera pose refinement framework, GSLoc. This framework
    enhances the localization accuracy of state-of-the-art absolute pose regression
    and scene coordinate regression methods. The 3DGS model renders high-quality synthetic
    images and depth maps to facilitate the establishment of 2D-3D correspondences.
    GSLoc obviates the need for training feature extractors or descriptors by operating
    directly on RGB images, utilizing the 3D foundation model, MASt3R, for precise
    2D matching. To improve the robustness of our model in challenging outdoor environments,
    we incorporate an exposure-adaptive module within the 3DGS framework. Consequently,
    GSLoc enables efficient one-shot pose refinement given a single RGB query and
    a coarse initial pose estimation. Our proposed approach surpasses leading NeRF-based
    optimization methods in both accuracy and runtime across indoor and outdoor visual
    localization benchmarks, achieving new state-of-the-art accuracy on two indoor
    datasets.
  project_page: https://gsloc.active.vision/
  paper: https://arxiv.org/pdf/2408.11085
  code: null
  video: null
  tags:
  - Poses
  - Project
  thumbnail: assets/thumbnails/liu2024gsloc.jpg
  publication_date: '2024-08-20T17:58:23+00:00'
- id: zhu2024loopsplat
  title: 'LoopSplat: Loop Closure by Registering 3D Gaussian Splats'
  authors: Liyuan Zhu, Yue Li, Erik Sandström, Shengyu Huang, Konrad Schindler, Iro
    Armeni
  year: '2024'
  abstract: Simultaneous Localization and Mapping (SLAM) based on 3D Gaussian Splats
    (3DGS) has recently shown promise towards more accurate, dense 3D scene maps.
    However, existing 3DGS-based methods fail to address the global consistency of
    the scene via loop closure and/or global bundle adjustment. To this end, we propose
    LoopSplat, which takes RGB-D images as input and performs dense mapping with 3DGS
    submaps and frame-to-model tracking. LoopSplat triggers loop closure online and
    computes relative loop edge constraints between submaps directly via 3DGS registration,
    leading to improvements in efficiency and accuracy over traditional global-to-local
    point cloud registration. It uses a robust pose graph optimization formulation
    and rigidly aligns the submaps to achieve global consistency. Evaluation on the
    synthetic Replica and real-world TUM-RGBD, ScanNet, and ScanNet++ datasets demonstrates
    competitive or superior tracking, mapping, and rendering compared to existing
    methods for dense RGB-D SLAM.
  project_page: https://loopsplat.github.io/
  paper: https://arxiv.org/pdf/2408.10154
  code: https://github.com/GradientSpaces/LoopSplat
  video: null
  tags:
  - Code
  - Project
  - SLAM
  thumbnail: assets/thumbnails/zhu2024loopsplat.jpg
  publication_date: '2024-08-19T17:04:18+00:00'
- id: lee2024rethinking
  title: Rethinking Open-Vocabulary Segmentation of Radiance Fields in 3D Space
  authors: Hyunjee Lee*, Youngsik Yun*, Jeongmin Bae, Seoha Kim, Youngjung Uh
  year: '2024'
  abstract: 'Understanding the 3D semantics of a scene is a fundamental problem for
    various scenarios such as embodied agents. While NeRFs and 3DGS excel at novel-view
    synthesis, previous methods for understanding their semantics have been limited
    to incomplete 3D understanding: their segmentation results are 2D masks and their
    supervision is anchored at 2D pixels. This paper revisits the problem set to pursue
    a better 3D understanding of a scene modeled by NeRFs and 3DGS as follows. 1)
    We directly supervise the 3D points to train the language embedding field. It
    achieves state-of-the-art accuracy without relying on multi-scale language embeddings.
    2) We transfer the pre-trained language field to 3DGS, achieving the first real-time
    rendering speed without sacrificing training time or accuracy. 3) We introduce
    a 3D querying and evaluation protocol for assessing the reconstructed geometry
    and semantics together. Code, checkpoints, and annotations will be available online.'
  project_page: https://hyunji12.github.io/Open3DRF/
  paper: https://arxiv.org/pdf/2408.07416
  code: https://github.com/hyunji12/Open3DRF
  video: null
  tags:
  - Code
  - Language Embedding
  - Project
  - Segmentation
  thumbnail: assets/thumbnails/lee2024rethinking.jpg
  publication_date: '2024-08-14T09:50:02+00:00'
- id: zheng2024headgap
  title: 'HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors'
  authors: Xiaozheng Zheng, Chao Wen, Zhaohu Li, Weiyi Zhang, Zhuo Su, Xu Chang, Yang
    Zhao, Zheng Lv, Xiaoyuan Zhang, Yongjie Zhang, Guidong Wang, Lan Xu
  year: '2024'
  abstract: 'In this paper, we present a novel 3D head avatar creation approach capable
    of generalizing from few-shot in-the-wild data with high-fidelity and animatable
    robustness. Given the underconstrained nature of this problem, incorporating prior
    knowledge is essential. Therefore, we propose a framework comprising prior learning
    and avatar creation phases. The prior learning phase leverages 3D head priors
    derived from a large-scale multi-view dynamic dataset, and the avatar creation
    phase applies these priors for few-shot personalization. Our approach effectively
    captures these priors by utilizing a Gaussian Splatting-based auto-decoder network
    with part-based dynamic modeling. Our method employs identity-shared encoding
    with personalized latent codes for individual identities to learn the attributes
    of Gaussian primitives. During the avatar creation phase, we achieve fast head
    avatar personalization by leveraging inversion and fine-tuning strategies. Extensive
    experiments demonstrate that our model effectively exploits head priors and successfully
    generalizes them to few-shot personalization, achieving photo-realistic rendering
    quality, multi-view consistency, and stable animation.

    '
  project_page: https://headgap.github.io/
  paper: https://arxiv.org/pdf/2408.06019.pdf
  code: null
  video: null
  tags:
  - Avatar
  - Dynamic
  - Project
  thumbnail: assets/thumbnails/zheng2024headgap.jpg
  publication_date: '2024-08-12T09:19:38+00:00'
  date_source: arxiv
- id: chahe2024query3d
  title: 'Query3D: LLM-Powered Open-Vocabulary Scene Segmentation with Language Embedded
    3D Gaussian'
  authors: Amirhosein Chahe, Lifeng Zhou
  year: '2024'
  abstract: 'This paper introduces a novel method for open-vocabulary 3D scene querying
    in autonomous driving by combining Language Embedded 3D Gaussians with Large Language
    Models (LLMs). We propose utilizing LLMs to generate both contextually canonical
    phrases and helping positive words for enhanced segmentation and scene interpretation.
    Our method leverages GPT-3.5 Turbo as an expert model to create a high-quality
    text dataset, which we then use to fine-tune smaller, more efficient LLMs for
    on-device deployment. Our comprehensive evaluation on the WayveScenes101 dataset
    demonstrates that LLM-guided segmentation significantly outperforms traditional
    approaches based on predefined canonical phrases. Notably, our fine-tuned smaller
    models achieve performance comparable to larger expert models while maintaining
    faster inference times. Through ablation studies, we discover that the effectiveness
    of helping positive words correlates with model scale, with larger models better
    equipped to leverage additional semantic information. This work represents a significant
    advancement towards more efficient, context-aware autonomous driving systems,
    effectively bridging 3D scene representation with high-level semantic querying
    while maintaining practical deployment considerations.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2408.03516.pdf
  code: https://github.com/Zhourobotics/Query-3DGS-LLM
  video: null
  tags:
  - Code
  - Language Embedding
  - Segmentation
  thumbnail: assets/thumbnails/chahe2024query3d.jpg
  publication_date: '2024-08-07T02:54:43+00:00'
  date_source: arxiv
- id: kaleta2024lumigauss
  title: 'LumiGauss: Relightable Gaussian Splatting in the Wild'
  authors: Joanna Kaleta, Kacper Kania, Tomasz Trzcinski, Marek Kowalski
  year: '2024'
  abstract: 'Decoupling lighting from geometry using unconstrained photo collections
    is notoriously challenging. Solving it would benefit many users as creating complex
    3D assets takes days of manual labor. Many previous works have attempted to address
    this issue, often at the expense of output fidelity, which questions the practicality
    of such methods. We introduce LumiGauss - a technique that tackles 3D reconstruction
    of scenes and environmental lighting through 2D Gaussian Splatting. Our approach
    yields high-quality scene reconstructions and enables realistic lighting synthesis
    under novel environment maps. We also propose a method for enhancing the quality
    of shadows, common in outdoor scenes, by exploiting spherical harmonics properties.
    Our approach facilitates seamless integration with game engines and enables the
    use of fast precomputed radiance transfer. We validate our method on the NeRF-OSR
    dataset, demonstrating superior performance over baseline methods. Moreover, LumiGauss
    can synthesize realistic images for unseen environment maps. Our code: https://github.com/joaxkal/lumigauss.

    '
  project_page: https://lumigauss.github.io/
  paper: https://arxiv.org/pdf/2408.04474.pdf
  code: https://github.com/joaxkal/lumigauss
  video: null
  tags:
  - Code
  - In the Wild
  - Project
  - Relight
  thumbnail: assets/thumbnails/kaleta2024lumigauss.jpg
  publication_date: '2024-08-06T23:41:57+00:00'
  date_source: arxiv
- id: wildersmith2024radiance
  title: Radiance Fields for Robotic Teleoperation
  authors: Maximum Wilder-Smith, Vaishakh Patil, Marco Hutter
  year: '2024'
  abstract: 'Radiance field methods such as Neural Radiance Fields (NeRFs) or 3D Gaussian
    Splatting (3DGS), have revolutionized graphics and novel view synthesis. Their
    ability to synthesize new viewpoints with photo-realistic quality, as well as
    capture complex volumetric and specular scenes, makes them an ideal visualization
    for robotic teleoperation setups. Direct camera teleoperation provides high-fidelity
    operation at the cost of maneuverability, while reconstruction-based approaches
    offer controllable scenes with lower fidelity. With this in mind, we propose replacing
    the traditional reconstruction-visualization components of the robotic teleoperation
    pipeline with online Radiance Fields, offering highly maneuverable scenes with
    photorealistic quality. As such, there are three main contributions to state of
    the art: (1) online training of Radiance Fields using live data from multiple
    cameras, (2) support for a variety of radiance methods including NeRF and 3DGS,
    (3) visualization suite for these methods including a virtual reality scene. To
    enable seamless integration with existing setups, these components were tested
    with multiple robots in multiple configurations and were displayed using traditional
    tools as well as the VR headset. The results across methods and robots were compared
    quantitatively to a baseline of mesh reconstruction, and a user study was conducted
    to compare the different visualization methods.'
  project_page: https://leggedrobotics.github.io/rffr.github.io/
  paper: https://arxiv.org/pdf/2407.20194
  code: https://github.com/leggedrobotics/radiance_field_ros
  video: null
  tags:
  - Code
  - Misc
  - Project
  - Robotics
  thumbnail: assets/thumbnails/wildersmith2024radiance.jpg
  publication_date: '2024-07-29T17:20:55+00:00'
- id: bao20243d
  title: '3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities'
  authors: Yanqi Bao, Tianyu Ding, Jing Huo, Yaoli Liu, Yuxin Li, Wenbin Li, Yang
    Gao, Jiebo Luo
  year: '2024'
  abstract: '3D Gaussian Splatting (3DGS) has emerged as a prominent technique with
    the potential to become a mainstream method for 3D representations. It can effectively
    transform multi-view images into explicit 3D Gaussian through efficient training,
    and achieve real-time rendering of novel views. This survey aims to analyze existing
    3DGS-related works from multiple intersecting perspectives, including related
    tasks, technologies, challenges, and opportunities. The primary objective is to
    provide newcomers with a rapid understanding of the field and to assist researchers
    in methodically organizing existing technologies and challenges. Specifically,
    we delve into the optimization, application, and extension of 3DGS, categorizing
    them based on their focuses or motivations. Additionally, we summarize and classify
    nine types of technical modules and corresponding improvements identified in existing
    works. Based on these analyses, we further examine the common challenges and technologies
    across various tasks, proposing potential research opportunities.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2407.17418.pdf
  code: null
  video: null
  tags:
  - Review
  thumbnail: assets/thumbnails/bao20243d.jpg
  publication_date: '2024-07-24T16:53:17+00:00'
  date_source: arxiv
- id: moenne-loccoz20243d
  title: '3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes'
  authors: Nicolas Moenne-Loccoz, Ashkan Mirzaei, Or Perel, Riccardo de Lutio, Janick
    Martinez Esturo, Gavriel State, Sanja Fidler, Nicholas Sharp, Zan Gojcic
  year: '2024'
  abstract: 'Particle-based representations of radiance fields such as 3D Gaussian
    Splatting have found great success for reconstructing and re-rendering of complex
    scenes. Most existing methods render particles via rasterization, projecting them
    to screen space tiles for processing in a sorted order. This work instead considers
    ray tracing the particles, building a bounding volume hierarchy and casting a
    ray for each pixel using high-performance GPU ray tracing hardware. To efficiently
    handle large numbers of semi-transparent particles, we describe a specialized
    rendering algorithm which encapsulates particles with bounding meshes to leverage
    fast ray-triangle intersections, and shades batches of intersections in depth-order.
    The benefits of ray tracing are well-known in computer graphics: processing incoherent
    rays for secondary lighting effects such as shadows and reflections, rendering
    from highly-distorted cameras common in robotics, stochastically sampling rays,
    and more. With our renderer, this flexibility comes at little cost compared to
    rasterization. Experiments demonstrate the speed and accuracy of our approach,
    as well as several applications in computer graphics and vision. We further propose
    related improvements to the basic Gaussian representation, including a simple
    use of generalized kernel functions which significantly reduces particle hit counts.

    '
  project_page: https://gaussiantracer.github.io/
  paper: https://arxiv.org/pdf/2407.07090.pdf
  code: null
  video: https://gaussiantracer.github.io/res/3dgrt_supplementary_video.mp4
  tags:
  - Project
  - Ray Tracing
  - Video
  thumbnail: assets/thumbnails/moenne-loccoz20243d.jpg
  publication_date: '2024-07-09T17:59:30+00:00'
  date_source: arxiv
- id: ji2024segment
  title: Segment Any 4D Gaussians
  authors: Shengxiang Ji, Guanjun Wu, Jiemin Fang, Jiazhong Cen, Taoran Yi, Wenyu
    Liu, Qi Tian, Xinggang Wang
  year: '2024'
  abstract: 'Modeling, understanding, and reconstructing the real world are crucial
    in XR/VR. Recently, 3D Gaussian Splatting (3D-GS) methods have shown remarkable
    success in modeling and understanding 3D scenes. Similarly, various 4D representations
    have demonstrated the ability to capture the dynamics of the 4D world. However,
    there is a dearth of research focusing on segmentation within 4D representations.
    In this paper, we propose Segment Any 4D Gaussians (SA4D), one of the first frameworks
    to segment anything in the 4D digital world based on 4D Gaussians. In SA4D, an
    efficient temporal identity feature field is introduced to handle Gaussian drifting,
    with the potential to learn precise identity features from noisy and sparse input.
    Additionally, a 4D segmentation refinement process is proposed to remove artifacts.
    Our SA4D achieves precise, high-quality segmentation within seconds in 4D Gaussians
    and shows the ability to remove, recolor, compose, and render high-quality anything
    masks. More demos are available at: https://jsxzs.github.io/sa4d/.'
  project_page: https://jsxzs.github.io/sa4d/
  paper: https://arxiv.org/pdf/2407.04504
  code: null
  video: null
  tags:
  - Project
  - Segmentation
  thumbnail: assets/thumbnails/ji2024segment.jpg
  publication_date: '2024-07-05T13:44:15+00:00'
- id: lee2024gscore
  title: 'GSCore: Efficient Radiance Field Rendering via Architectural Support for
    3D Gaussian Splatting'
  authors: Junseo Lee, Seokwon Lee, Jungi Lee, Junyong Park, Jaewoong Sim
  year: '2024'
  abstract: This paper presents GSCore, a hardware acceleration unit that efficiently
    executes the rendering pipeline of 3D Gaussian Splatting with algorithmic optimizations.
    GSCore builds on the observations from an in-depth analysis of Gaussian-based
    radiance field rendering to enhance computational efficiency and bring the technique
    to wide adoption. In doing so, we present several optimization techniques, Gaussian
    shape-aware intersection test, hierarchical sorting, and subtile skipping, all
    of which are synergistically integrated with GSCore. We implement the hardware
    design of GSCore, synthesize it using a commercial 28nm technology, and evaluate
    the performance across a range of synthetic and real-world scenes with varying
    image resolutions. Our evaluation results show that GSCore achieves a 15.86× speedup
    on average over the mobile consumer GPU with a substantially smaller area and
    lower energy consumption.
  project_page: null
  paper: https://jaewoong.org/pubs/asplos24-gscore.pdf
  code: null
  video: https://youtu.be/TByYGw837IU?si=7zBe0yqpsJUoVbIV
  tags:
  - Acceleration
  - Rendering
  thumbnail: assets/thumbnails/lee2024gscore.jpg
  publication_date: '2024-07-01T00:00:00'
  date_source: estimated
- id: kim2024optimizing
  title: Optimizing Dynamic NeRF and 3DGS with No Video Synchronization
  authors: Seoha Kim*, Jeongmin Bae*, Youngsik Yun, HyunSeung Son, Hahyun Lee, Gun
    Bang, Youngjung Uh
  year: '2024'
  abstract: Recent advancements in 4D scene reconstruction using dynamic NeRF and
    3DGS have demonstrated the ability to represent dynamic scenes from multi-view
    videos. However, they fail to reconstruct the dynamic scenes and struggle to fit
    even the training views in unsynchronized settings. It happens because they employ
    a single latent embedding for a frame, while the multi-view images at the same
    frame were actually captured at different moments. To address this limitation,
    we introduce time offsets for individual unsynchronized videos and jointly optimize
    the offsets with the field. By design, our method is applicable for various baselines,
    even regardless of the types of radiance fields. We conduct experiments on the
    common Plenoptic Video Dataset and a newly built Unsynchronized Dynamic Blender
    Dataset to verify the performance of our method.
  project_page: null
  paper: https://openreview.net/pdf?id=RQutkn4V9I
  code: null
  video: null
  tags:
  - Dynamic
  thumbnail: assets/thumbnails/kim2024optimizing.jpg
  publication_date: '2024-07-01T00:00:00'
  date_source: estimated
- id: zhang2024egogaussian
  title: 'EgoGaussian: Dynamic Scene Understanding from Egocentric Video with 3D Gaussian
    Splatting'
  authors: Daiwei Zhang, Gengyan Li, Jiajie Li, Mickaël Bressieux, Otmar Hilliges,
    Marc Pollefeys, Luc Van Gool, Xi Wang
  year: '2024'
  abstract: Human activities are inherently complex, often involving numerous object
    interactions. To better understand these activities, it is crucial to model their
    interactions with the environment captured through dynamic changes. The recent
    availability of affordable head-mounted cameras and egocentric data offers a more
    accessible and efficient means to understand human-object interactions in 3D environments.
    However, most existing methods for human activity modeling neglect the dynamic
    interactions with objects, resulting in only static representations. The few existing
    solutions often require inputs from multiple sources, including multi-camera setups,
    depth-sensing cameras, or kinesthetic sensors. To this end, we introduce EgoGaussian,
    the first method capable of simultaneously reconstructing 3D scenes and dynamically
    tracking 3D object motion from RGB egocentric input alone. We leverage the uniquely
    discrete nature of Gaussian Splatting and segment dynamic interactions from the
    background, with both having explicit representations. Our approach employs a
    clip-level online learning pipeline that leverages the dynamic nature of human
    activities, allowing us to reconstruct the temporal evolution of the scene in
    chronological order and track rigid object motion. EgoGaussian shows significant
    improvements in terms of both dynamic object and background reconstruction quality
    compared to the state-of-the-art. We also qualitatively demonstrate the high quality
    of the reconstructed models.
  project_page: https://zdwww.github.io/egogs.github.io/
  paper: https://arxiv.org/pdf/2406.19811
  code: https://github.com/zdwww/EgoGaussian
  video: https://youtu.be/nsZrmM7CJB0?si=IJnfWH_Vf_UW2JoF
  tags:
  - Code
  - Dynamic
  - Project
  - Video
  thumbnail: assets/thumbnails/zhang2024egogaussian.jpg
  publication_date: '2024-06-28T10:39:36+00:00'
- id: stearns2024dynamic
  title: Dynamic Gaussian Marbles for Novel View Synthesis of Casual Monocular Videos
  authors: Colton Stearns, Adam Harley, Mikaela Uy, Florian Dubost, Federico Tombari,
    Gordon Wetzstein, Leonidas Guibas
  year: '2024'
  abstract: 'Gaussian splatting has become a popular representation for novel-view
    synthesis, exhibiting clear strengths in efficiency, photometric quality, and
    compositional edibility. Following its success, many works have extended Gaussians
    to 4D, showing that dynamic Gaussians maintain these benefits while also tracking
    scene geometry far better than alternative representations. Yet, these methods
    assume dense multi-view videos as supervision. In this work, we are interested
    in extending the capability of Gaussian scene representations to casually captured
    monocular videos. We show that existing 4D Gaussian methods dramatically fail
    in this setup because the monocular setting is underconstrained. Building off
    this finding, we propose a method we call Dynamic Gaussian Marbles, which consist
    of three core modifications that target the difficulties of the monocular setting.
    First, we use isotropic Gaussian "marbles'''', reducing the degrees of freedom
    of each Gaussian. Second, we employ a hierarchical divide and-conquer learning
    strategy to efficiently guide the optimization towards solutions with globally
    coherent motion. Finally, we add image-level and geometry-level priors into the
    optimization, including a tracking loss that takes advantage of recent progress
    in point tracking. By constraining the optimization, Dynamic Gaussian Marbles
    learns Gaussian trajectories that enable novel-view rendering and accurately capture
    the 3D motion of the scene elements. We evaluate on the Nvidia Dynamic Scenes
    dataset and the DyCheck iPhone dataset, and show that Gaussian Marbles significantly
    outperforms other Gaussian baselines in quality, and is on-par with non-Gaussian
    representations, all while maintaining the efficiency, compositionality, editability,
    and tracking benefits of Gaussians. Our project page can be found here https://geometry.stanford.edu/projects/dynamic-gaussian-marbles.github.io/.

    '
  project_page: https://geometry.stanford.edu/projects/dynamic-gaussian-marbles.github.io/
  paper: https://arxiv.org/pdf/2406.18717.pdf
  code: https://github.com/coltonstearns/dynamic-gaussian-marbles
  video: null
  tags:
  - Code
  - Dynamic
  - Monocular
  - Project
  thumbnail: assets/thumbnails/stearns2024dynamic.jpg
  publication_date: '2024-06-26T19:37:07+00:00'
  date_source: arxiv
- id: zhao2024on
  title: On Scaling Up 3D Gaussian Splatting Training
  authors: Hexu Zhao, Haoyang Weng, Daohan Lu, Ang Li, Jinyang Li, Aurojit Panda,
    Saining Xie
  year: '2024'
  abstract: '3D Gaussian Splatting (3DGS) is increasingly popular for 3D reconstruction
    due to its superior visual quality and rendering speed. However, 3DGS training
    currently occurs on a single GPU, limiting its ability to handle high-resolution
    and large-scale 3D reconstruction tasks due to memory constraints. We introduce
    Grendel, a distributed system designed to partition 3DGS parameters and parallelize
    computation across multiple GPUs. As each Gaussian affects a small, dynamic subset
    of rendered pixels, Grendel employs sparse all-to-all communication to transfer
    the necessary Gaussians to pixel partitions and performs dynamic load balancing.
    Unlike existing 3DGS systems that train using one camera view image at a time,
    Grendel supports batched training with multiple views. We explore various optimization
    hyperparameter scaling strategies and find that a simple sqrt(batch size) scaling
    rule is highly effective. Evaluations using large-scale, high-resolution scenes
    show that Grendel enhances rendering quality by scaling up 3DGS parameters across
    multiple GPUs. On the Rubble dataset, we achieve a test PSNR of 27.28 by distributing
    40.4 million Gaussians across 16 GPUs, compared to a PSNR of 26.28 using 11.2
    million Gaussians on a single GPU. Grendel is an open-source project available
    at: https://github.com/nyu-systems/Grendel-GS'
  project_page: https://daohanlu.github.io/scaling-up-3dgs/
  paper: https://arxiv.org/pdf/2406.18533
  code: https://github.com/nyu-systems/Grendel-GS
  video: https://youtu.be/WaYfY3GTs6U
  tags:
  - Code
  - Distributed
  - Large-Scale
  - Project
  - Video
  thumbnail: assets/thumbnails/zhao2024on.jpg
  publication_date: '2024-06-26T17:59:28+00:00'
- id: li2024gsoctree
  title: 'GS-Octree: Octree-based 3D Gaussian Splatting for Robust Object-level 3D
    Reconstruction Under Strong Lighting'
  authors: Jiaze Li, Zhengyu Wen, Luo Zhang, Jiangbei Hu, Fei Hou, Zhebin Zhang, Ying
    He
  year: '2024'
  abstract: 'The 3D Gaussian Splatting technique has significantly advanced the construction
    of radiance fields from multi-view images, enabling real-time rendering. While
    point-based rasterization effectively reduces computational demands for rendering,
    it often struggles to accurately reconstruct the geometry of the target object,
    especially under strong lighting. To address this challenge, we introduce a novel
    approach that combines octree-based implicit surface representations with Gaussian
    splatting. Our method consists of four stages. Initially, it reconstructs a signed
    distance field (SDF) and a radiance field through volume rendering, encoding them
    in a low-resolution octree. The initial SDF represents the coarse geometry of
    the target object. Subsequently, it introduces 3D Gaussians as additional degrees
    of freedom, which are guided by the SDF. In the third stage, the optimized Gaussians
    further improve the accuracy of the SDF, allowing it to recover finer geometric
    details compared to the initial SDF obtained in the first stage. Finally, it adopts
    the refined SDF to further optimize the 3D Gaussians via splatting, eliminating
    those that contribute little to visual appearance. Experimental results show that
    our method, which leverages the distribution of 3D Gaussians with SDFs, reconstructs
    more accurate geometry, particularly in images with specular highlights caused
    by strong lighting.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2406.18199.pdf
  code: null
  video: null
  tags:
  - Meshing
  thumbnail: assets/thumbnails/li2024gsoctree.jpg
  publication_date: '2024-06-26T09:29:56+00:00'
  date_source: arxiv
- id: papantonakis2024reducing
  title: Reducing the Memory Footprint of 3D Gaussian Splatting
  authors: Panagiotis Papantonakis, Georgios Kopanas, Bernhard Kerbl, Alexandre Lanvin,
    George Drettakis
  year: '2024'
  abstract: '3D Gaussian splatting provides excellent visual quality for novel view
    synthesis, with fast training and realtime rendering; unfortunately, the memory
    requirements of this method for storing and transmission are unreasonably high.
    We first analyze the reasons for this, identifying three main areas where storage
    can be reduced: the number of 3D Gaussian primitives used to represent a scene,
    the number of coefficients for the spherical harmonics used to represent directional
    radiance, and the precision required to store Gaussian primitive attributes. We
    present a solution to each of these issues. First, we propose an efficient, resolutionaware
    primitive pruning approach, reducing the primitive count by half. Second, we introduce
    an adaptive adjustment method to choose the number of coefficients used to represent
    directional radiance for each Gaussian primitive, and finally a codebook-based
    quantization method, together with a half-float representation for further memory
    reduction. Taken together, these three components result in a ×27 reduction in
    overall size on disk on the standard datasets we tested, along with a x1.7 speedup
    in rendering speed. We demonstrate our method on standard datasets and show how
    our solution results in significantly reduced download times when using the method
    on a mobile device (see Fig. 1).'
  project_page: https://repo-sam.inria.fr/fungraph/reduced_3dgs/
  paper: https://arxiv.org/pdf/2406.17074.pdf
  code: https://repo-sam.inria.fr/fungraph/reduced_3dgs
  video: https://www.youtube.com/watch?v=EnKE-d7eMds&t=48s
  tags:
  - Code
  - Compression
  - Project
  - Video
  thumbnail: assets/thumbnails/papantonakis2024reducing.jpg
  publication_date: '2024-06-24T19:01:44+00:00'
  date_source: arxiv
- id: mallick2024taming
  title: 'Taming 3DGS: High-Quality Radiance Fields with Limited Resources'
  authors: Saswat Subhajyoti Mallick, Rahul Goel, Bernhard Kerbl, Francisco Vicente
    Carrasco, Markus Steinberger, Fernando De La Torre
  year: '2024'
  abstract: '3D Gaussian Splatting (3DGS) has transformed novel-view synthesis with
    its fast, interpretable, and high-fidelity rendering. However, its resource requirements
    limit its usability. Especially on constrained devices, training performance degrades
    quickly and often cannot complete due to excessive memory consumption of the model.
    The method converges with an indefinite number of Gaussians -- many of them redundant
    -- making rendering unnecessarily slow and preventing its usage in downstream
    tasks that expect fixed-size inputs. To address these issues, we tackle the challenges
    of training and rendering 3DGS models on a budget. We use a guided, purely constructive
    densification process that steers densification toward Gaussians that raise the
    reconstruction quality. Model size continuously increases in a controlled manner
    towards an exact budget, using score-based densification of Gaussians with training-time
    priors that measure their contribution. We further address training speed obstacles:
    following a careful analysis of 3DGS'' original pipeline, we derive faster, numerically
    equivalent solutions for gradient computation and attribute updates, including
    an alternative parallelization for efficient backpropagation. We also propose
    quality-preserving approximations where suitable to reduce training time even
    further. Taken together, these enhancements yield a robust, scalable solution
    with reduced training times, lower compute and memory requirements, and high quality.
    Our evaluation shows that in a budgeted setting, we obtain competitive quality
    metrics with 3DGS while achieving a 4--5x reduction in both model size and training
    time. With more generous budgets, our measured quality surpasses theirs. These
    advances open the door for novel-view synthesis in constrained environments, e.g.,
    mobile devices.

    '
  project_page: https://humansensinglab.github.io/taming-3dgs/
  paper: https://arxiv.org/pdf/2406.15643.pdf
  code: https://github.com/humansensinglab/taming-3dgs
  video: https://youtu.be/iiUXqYmezbY
  tags:
  - Acceleration
  - Code
  - Densification
  - Project
  - Video
  thumbnail: assets/thumbnails/mallick2024taming.jpg
  publication_date: '2024-06-21T20:44:23+00:00'
- id: sun2024splatter
  title: 'Splatter a Video: Video Gaussian Representation for Versatile Processing'
  authors: Yang-Tian Sun, Yi-Hua Huang, Lin Ma, Xiaoyang Lyu, Yan-Pei Cao, Xiaojuan
    Qi
  year: '2024'
  abstract: 'Video representation is a long-standing problem that is crucial for various
    down-stream tasks, such as tracking,depth prediction,segmentation,view synthesis,and
    editing. However, current methods either struggle to model complex motions due
    to the absence of 3D structure or rely on implicit 3D representations that are
    ill-suited for manipulation tasks. To address these challenges, we introduce a
    novel explicit 3D representation-video Gaussian representation -- that embeds
    a video into 3D Gaussians. Our proposed representation models video appearance
    in a 3D canonical space using explicit Gaussians as proxies and associates each
    Gaussian with 3D motions for video motion. This approach offers a more intrinsic
    and explicit representation than layered atlas or volumetric pixel matrices. To
    obtain such a representation, we distill 2D priors, such as optical flow and depth,
    from foundation models to regularize learning in this ill-posed setting. Extensive
    applications demonstrate the versatility of our new video representation. It has
    been proven effective in numerous video processing tasks, including tracking,
    consistent video depth and feature refinement, motion and appearance editing,
    and stereoscopic video generation. Project page: https://sunyangtian.github.io/spatter_a_video_web/

    '
  project_page: https://sunyangtian.github.io/spatter_a_video_web/
  paper: https://arxiv.org/pdf/2406.13870.pdf
  code: null
  video: null
  tags:
  - Project
  - Video
  thumbnail: assets/thumbnails/sun2024splatter.jpg
  publication_date: '2024-06-19T22:20:03+00:00'
  date_source: arxiv
- id: pan2023humansplat
  title: 'HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure
    Priors'
  authors: Panwang Pan, Zhuo Su, Chenguo Lin, Zhen Fan, Yongjie Zhang, Zeming Li,
    Tingting Shen, Yadong Mu, Yebin Liu
  year: '2023'
  abstract: 'Despite recent advancements in high-fidelity human reconstruction techniques,
    the requirements for densely captured images or time-consuming per-instance optimization
    significantly hinder their applications in broader scenarios. To tackle these
    issues, we present HumanSplat that predicts the 3D Gaussian Splatting properties
    of any human from a single input image in a generalizable manner. In particular,
    HumanSplat comprises a 2D multi-view diffusion model and a latent reconstruction
    transformer with human structure priors that adeptly integrate geometric priors
    and semantic features within a unified framework. A hierarchical loss that incorporates
    human semantic information is further designed to achieve high-fidelity texture
    modeling and better constrain the estimated multiple views. Comprehensive experiments
    on standard benchmarks and in-the-wild images demonstrate that HumanSplat surpasses
    existing state-of-the-art methods in achieving photorealistic novel-view synthesis.
    Project page: https://humansplat.github.io/.'
  project_page: https://humansplat.github.io/
  paper: https://arxiv.org/pdf/2406.12459
  code: https://github.com/humansplat/humansplat
  video: null
  tags:
  - Avatar
  - Code
  - Project
  thumbnail: assets/thumbnails/pan2023humansplat.jpg
  publication_date: '2024-06-18T10:05:33+00:00'
- id: kerbl2024a
  title: A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very
    Large Datasets
  authors: Bernhard Kerbl, Andreas Meuleman, Georgios Kopanas, Michael Wimmer,  Alexandre
    Lanvin, George Drettakis
  year: '2024'
  abstract: Novel view synthesis has seen major advances in recent years, with 3D
    Gaussian splatting offering an excellent level of visual quality, fast training
    and real-time rendering. However, the resources needed for training and rendering
    inevitably limit the size of the captured scenes that can be represented with
    good visual quality. We introduce a hierarchy of 3D Gaussians that preserves visual
    quality for very large scenes, while offering an efficient Level-of-Detail (LOD)
    solution for efficient rendering of distant content with effective level selection
    and smooth transitions between levels. We introduce a divide-and-conquer approach
    that allows us to train very large scenes in independent chunks. We consolidate
    the chunks into a hierarchy that can be optimized to further improve visual quality
    of Gaussians merged into intermediate nodes. Very large captures typically have
    sparse coverage of the scene, presenting many challenges to the original 3D Gaussian
    splatting training method; we adapt and regularize training to account for these
    issues. We present a complete solution, that enables real-time rendering of very
    large scenes and can adapt to available resources thanks to our LOD method. We
    show results for captured scenes with up to tens of thousands of images with a
    simple and affordable rig, covering trajectories of up to several kilometers and
    lasting up to one hour.
  project_page: https://repo-sam.inria.fr/fungraph/hierarchical-3d-gaussians/
  paper: https://arxiv.org/pdf/2406.12080.pdf
  code: https://github.com/graphdeco-inria/hierarchical-3d-gaussians
  video: https://repo-sam.inria.fr/fungraph/hierarchical-3d-gaussians/content/videos/small_city.mp4
  tags:
  - Code
  - Large-Scale
  - Project
  - Video
  thumbnail: assets/thumbnails/kerbl2024a.jpg
  publication_date: '2024-06-17T20:40:18+00:00'
  date_source: arxiv
- id: hyung2024effective
  title: Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splatting
  authors: Junha Hyung, Susung Hong, Sungwon Hwang, Jaeseong Lee, Jaegul Choo, Jin-Hwa
    Kim
  year: '2024'
  abstract: 3D reconstruction from multi-view images is one of the fundamental challenges
    in computer vision and graphics. Recently, 3D Gaussian Splatting (3DGS) has emerged
    as a promising technique capable of real-time rendering with high-quality 3D reconstruction.
    This method utilizes 3D Gaussian representation and tile-based splatting techniques,
    bypassing the expensive neural field querying. Despite its potential, 3DGS encounters
    challenges, including needle-like artifacts, suboptimal geometries, and inaccurate
    normals, due to the Gaussians converging into anisotropic Gaussians with one dominant
    variance. We propose using effective rank analysis to examine the shape statistics
    of 3D Gaussian primitives, and identify the Gaussians indeed converge into needle-like
    shapes with the effective rank 1. To address this, we introduce effective rank
    as a regularization, which constrains the structure of the Gaussians. Our new
    regularization method enhances normal and geometry reconstruction while reducing
    needle-like artifacts. The approach can be integrated as an add-on module to other
    3DGS variants, improving their quality without compromising visual fidelity.
  project_page: https://junhahyung.github.io/erankgs.github.io/
  paper: https://arxiv.org/pdf/2406.11672
  code: null
  video: null
  tags:
  - Densification
  - Meshing
  - Project
  thumbnail: assets/thumbnails/hyung2024effective.jpg
  publication_date: '2024-06-17T15:51:59+00:00'
- id: t20243dgszip
  title: '3DGS.zip: A survey on 3D Gaussian Splatting Compression Methods'
  authors: Milena T. Bagdasarian, Paul Knoll, Florian Barthel, Anna Hilsmann, Peter
    Eisert, Wieland Morgenstern
  year: '2024'
  abstract: We present a work-in-progress survey on 3D Gaussian Splatting compression
    methods, focusing on their statistical performance across various benchmarks.
    This survey aims to facilitate comparability by summarizing key statistics of
    different compression approaches in a tabulated format. The datasets evaluated
    include TanksAndTemples, MipNeRF360, DeepBlending, and SyntheticNeRF. For each
    method, we report the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity
    Index (SSIM), Learned Perceptual Image Patch Similarity (LPIPS), and the resultant
    size in megabytes (MB), as provided by the respective authors. This is an ongoing,
    open project, and we invite contributions from the research community as GitHub
    issues or pull requests. Please visit https://w-m.github.io/3dgs-compression-survey/
    for more information and a sortable version of the table.
  project_page: https://w-m.github.io/3dgs-compression-survey
  paper: https://arxiv.org/pdf/2407.09510
  code: null
  video: null
  tags:
  - Compression
  - Project
  thumbnail: assets/thumbnails/t20243dgszip.jpg
  publication_date: '2024-06-17T11:43:38+00:00'
- id: hanson2024pup
  title: 'PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting'
  authors: Alex Hanson, Allen Tu, Vasu Singla, Mayuka Jayawardhana, Matthias Zwicker,
    Tom Goldstein
  year: '2024'
  abstract: 'Recent advances in novel view synthesis have enabled real-time rendering
    speeds with high reconstruction accuracy. 3D Gaussian Splatting (3D-GS), a foundational
    point-based parametric 3D scene representation, models scenes as large sets of
    3D Gaussians. However, complex scenes can consist of millions of Gaussians, resulting
    in high storage and memory requirements that limit the viability of 3D-GS on devices
    with limited resources. Current techniques for compressing these pretrained models
    by pruning Gaussians rely on combining heuristics to determine which Gaussians
    to remove. At high compression ratios, these pruned scenes suffer from heavy degradation
    of visual fidelity and loss of foreground details. In this paper, we propose a
    principled sensitivity pruning score that preserves visual fidelity and foreground
    details at significantly higher compression ratios than existing approaches. It
    is computed as a second-order approximation of the reconstruction error on the
    training views with respect to the spatial parameters of each Gaussian. Additionally,
    we propose a multi-round prune-refine pipeline that can be applied to any pretrained
    3D-GS model without changing its training pipeline. After pruning 90% of Gaussians,
    a substantially higher percentage than previous methods, our PUP 3D-GS pipeline
    increases average rendering speed by 3.56$\times$ while retaining more salient
    foreground information and achieving higher image quality metrics than existing
    techniques on scenes from the Mip-NeRF 360, Tanks & Temples, and Deep Blending
    datasets.

    '
  project_page: https://pup3dgs.github.io/
  paper: https://arxiv.org/pdf/2406.10219.pdf
  code: https://github.com/j-alex-hanson/gaussian-splatting-pup
  video: null
  tags:
  - Code
  - Densification
  - Project
  thumbnail: assets/thumbnails/hanson2024pup.jpg
  publication_date: '2024-06-14T17:53:55+00:00'
  date_source: arxiv
- id: leroy2024grounding
  title: Grounding Image Matching in 3D with MASt3R
  authors: Vincent Leroy, Yohann Cabon, Jérôme Revaud
  year: '2024'
  abstract: 'Image Matching is a core component of all best-performing algorithms
    and pipelines in 3D vision. Yet despite matching being fundamentally a 3D problem,
    intrinsically linked to camera pose and scene geometry, it is typically treated
    as a 2D problem. This makes sense as the goal of matching is to establish correspondences
    between 2D pixel fields, but also seems like a potentially hazardous choice. In
    this work, we take a different stance and propose to cast matching as a 3D task
    with DUSt3R, a recent and powerful 3D reconstruction framework based on Transformers.
    Based on pointmaps regression, this method displayed impressive robustness in
    matching views with extreme viewpoint changes, yet with limited accuracy. We aim
    here to improve the matching capabilities of such an approach while preserving
    its robustness. We thus propose to augment the DUSt3R network with a new head
    that outputs dense local features, trained with an additional matching loss. We
    further address the issue of quadratic complexity of dense matching, which becomes
    prohibitively slow for downstream applications if not carefully treated. We introduce
    a fast reciprocal matching scheme that not only accelerates matching by orders
    of magnitude, but also comes with theoretical guarantees and, lastly, yields improved
    results. Extensive experiments show that our approach, coined MASt3R, significantly
    outperforms the state of the art on multiple matching tasks. In particular, it
    beats the best published methods by 30% (absolute improvement) in VCRE AUC on
    the extremely challenging Map-free localization dataset.

    '
  project_page: https://europe.naverlabs.com/blog/mast3r-matching-and-stereo-3d-reconstruction/
  paper: https://arxiv.org/pdf/2406.09756.pdf
  code: https://github.com/naver/mast3r
  video: null
  tags:
  - Code
  - Project
  thumbnail: assets/thumbnails/leroy2024grounding.jpg
  publication_date: '2024-06-14T06:46:30+00:00'
  date_source: arxiv
- id: kirschstein2024gghead
  title: 'GGHead: Fast and Generalizable 3D Gaussian Heads'
  authors: Tobias Kirschstein, Simon Giebenhain, Jiapeng Tang, Markos Georgopoulos,
    Matthias Nießner
  year: '2024'
  abstract: 'Learning 3D head priors from large 2D image collections is an important
    step towards high-quality 3D-aware human modeling. A core requirement is an efficient
    architecture that scales well to large-scale datasets and large image resolutions.
    Unfortunately, existing 3D GANs struggle to scale to generate samples at high
    resolutions due to their relatively slow train and render speeds, and typically
    have to rely on 2D superresolution networks at the expense of global 3D consistency.
    To address these challenges, we propose Generative Gaussian Heads (GGHead), which
    adopts the recent 3D Gaussian Splatting representation within a 3D GAN framework.
    To generate a 3D representation, we employ a powerful 2D CNN generator to predict
    Gaussian attributes in the UV space of a template head mesh. This way, GGHead
    exploits the regularity of the template''s UV layout, substantially facilitating
    the challenging task of predicting an unstructured set of 3D Gaussians. We further
    improve the geometric fidelity of the generated 3D representations with a novel
    total variation loss on rendered UV coordinates. Intuitively, this regularization
    encourages that neighboring rendered pixels should stem from neighboring Gaussians
    in the template''s UV space. Taken together, our pipeline can efficiently generate
    3D heads trained only from single-view 2D image observations. Our proposed framework
    matches the quality of existing 3D head GANs on FFHQ while being both substantially
    faster and fully 3D consistent. As a result, we demonstrate real-time generation
    and rendering of high-quality 3D-consistent heads at $1024^2$ resolution for the
    first time. Project Website: https://tobias-kirschstein.github.io/gghead

    '
  project_page: https://tobias-kirschstein.github.io/gghead/
  paper: https://arxiv.org/pdf/2406.09377.pdf
  code: https://github.com/tobias-kirschstein/gghead
  video: https://youtu.be/M5vq3DoZ7RI
  tags:
  - Code
  - GAN
  - Project
  - Video
  thumbnail: assets/thumbnails/kirschstein2024gghead.jpg
  publication_date: '2024-06-13T17:54:38+00:00'
  date_source: arxiv
- id: jaganathan2024iceg
  title: 'ICE-G: Image Conditional Editing of 3D Gaussian Splats'
  authors: Vishnu Jaganathan, Hannah Huang, Muhammad Zubair Irshad, Varun Jampani,
    Amit Raj, Zsolt Kira
  year: '2024'
  abstract: Recently many techniques have emerged to create high quality 3D assets
    and scenes. When it comes to editing of these objects, however, existing approaches
    are either slow, compromise on quality, or do not provide enough customization.
    We introduce a novel approach to quickly edit a 3D model from a single reference
    view. Our technique first segments the edit image, and then matches semantically
    corresponding regions across chosen segmented dataset views using DINO features.
    A color or texture change from a particular region of the edit image can then
    be applied to other views automatically in a semantically sensible manner. These
    edited views act as an updated dataset to further train and re-style the 3D scene.
    The end-result is therefore an edited 3D model. Our framework enables a wide variety
    of editing tasks such as manual local edits, correspondence based style transfer
    from any example image, and a combination of different styles from multiple example
    images. We use Gaussian Splats as our primary 3D representation due to their speed
    and ease of local editing, but our technique works for other methods such as NeRFs
    as well. We show through multiple examples that our method produces higher quality
    results while offering fine grained control of editing.
  project_page: https://ice-gaussian.github.io/
  paper: https://arxiv.org/pdf/2406.08488
  code: null
  video: https://youtu.be/dDsCwRXixp8?si=415s7-dEpM7-FPMq
  tags:
  - Editing
  - Project
  - Video
  thumbnail: assets/thumbnails/jaganathan2024iceg.jpg
  publication_date: '2024-06-12T17:59:52+00:00'
- id: fan2024trim
  title: Trim 3D Gaussian Splatting for Accurate Geometry Representation
  authors: Lue Fan, Yuxue Yang, Minxing Li, Hongsheng Li, Zhaoxiang Zhang
  year: '2024'
  abstract: In this paper, we introduce Trim 3D Gaussian Splatting (TrimGS) to reconstruct
    accurate 3D geometry from images. Previous arts for geometry reconstruction from
    3D Gaussians mainly focus on exploring strong geometry regularization. Instead,
    from a fresh perspective, we propose to obtain accurate 3D geometry of a scene
    by Gaussian trimming, which selectively removes the inaccurate geometry while
    preserving accurate structures. To achieve this, we analyze the contributions
    of individual 3D Gaussians and propose a contribution-based trimming strategy
    to remove the redundant or inaccurate Gaussians. Furthermore, our experimental
    and theoretical analyses reveal that a relatively small Gaussian scale is a non-negligible
    factor in representing and optimizing the intricate details. Therefore the proposed
    TrimGS maintains relatively small Gaussian scales. In addition, TrimGS is also
    compatible with the effective geometry regularization strategies in previous arts.
    When combined with the original 3DGS and the state-of-the-art 2DGS, TrimGS consistently
    yields more accurate geometry and higher perceptual quality.
  project_page: https://trimgs.github.io/
  paper: https://arxiv.org/pdf/2406.07499
  code: https://github.com/YuxueYang1204/TrimGS
  video: null
  tags:
  - 2DGS
  - Code
  - Densification
  - Project
  thumbnail: assets/thumbnails/fan2024trim.jpg
  publication_date: '2024-06-11T17:34:46+00:00'
- id: yu20244real
  title: '4Real: Towards Photorealistic 4D Scene Generation via Video Diffusion Models'
  authors: Heng Yu, Chaoyang Wang, Peiye Zhuang, Willi Menapace, Aliaksandr Siarohin,
    Junli Cao, Laszlo A Jeni, Sergey Tulyakov, Hsin-Ying Lee
  year: '2024'
  abstract: Existing dynamic scene generation methods mostly rely on distilling knowledge
    from pre-trained 3D generative models, which are typically fine-tuned on synthetic
    object datasets. As a result, the generated scenes are often object-centric and
    lack photorealism. To address these limitations, we introduce a novel pipeline
    designed for photorealistic text-to-4D scene generation, discarding the dependency
    on multi-view generative models and instead fully utilizing video generative models
    trained on diverse real-world datasets. Our method begins by generating a reference
    video using the video generation model. We then learn the canonical 3D representation
    of the video using a freeze-time video, delicately generated from the reference
    video. To handle inconsistencies in the freeze-time video, we jointly learn a
    per-frame deformation to model these imperfections. We then learn the temporal
    deformation based on the canonical representation to capture dynamic interactions
    in the reference video. The pipeline facilitates the generation of dynamic scenes
    with enhanced photorealism and structural integrity, viewable from multiple perspectives,
    thereby setting a new standard in 4D scene generation.
  project_page: https://snap-research.github.io/4Real/
  paper: https://arxiv.org/pdf/2406.07472.pdf
  code: null
  video: null
  tags:
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/yu20244real.jpg
  publication_date: '2024-06-11T17:19:26+00:00'
- id: jin2024lighting
  title: 'Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering
    for HDR View Synthesis'
  authors: Xin Jin, Pengyi Jiao, Zheng-Peng Duan, Xingchao Yang, Chun-Le Guo, Bo Ren,
    Chongyi Li
  year: '2024'
  abstract: 'Volumetric rendering based methods, like NeRF, excel in HDR view synthesis
    from RAWimages, especially for nighttime scenes. While, they suffer from long
    training times and cannot perform real-time rendering due to dense sampling requirements.
    The advent of 3D Gaussian Splatting (3DGS) enables real-time rendering and faster
    training. However, implementing RAW image-based view synthesis directly using
    3DGS is challenging due to its inherent drawbacks: 1) in nighttime scenes, extremely
    low SNR leads to poor structure-from-motion (SfM) estimation in distant views;
    2) the limited representation capacity of spherical harmonics (SH) function is
    unsuitable for RAW linear color space; and 3) inaccurate scene structure hampers
    downstream tasks such as refocusing. To address these issues, we propose LE3D
    (Lighting Every darkness with 3DGS). Our method proposes Cone Scatter Initialization
    to enrich the estimation of SfM, and replaces SH with a Color MLP to represent
    the RAW linear color space. Additionally, we introduce depth distortion and near-far
    regularizations to improve the accuracy of scene structure for downstream tasks.
    These designs enable LE3D to perform real-time novel view synthesis, HDR rendering,
    refocusing, and tone-mapping changes. Compared to previous volumetric rendering
    based methods, LE3D reduces training time to 1% and improves rendering speed by
    up to 4,000 times for 2K resolution images in terms of FPS. Code and viewer can
    be found in https://github.com/Srameo/LE3D .

    '
  project_page: https://srameo.github.io/projects/le3d/intro.htmla
  paper: https://arxiv.org/pdf/2406.06216.pdf
  code: https://github.com/Srameo/LE3D
  video: https://srameo.github.io/projects/le3d/assets/demo_video_interactive_viewer_compressed.mp4
  tags:
  - Code
  - Project
  - Rendering
  - Video
  thumbnail: assets/thumbnails/jin2024lighting.jpg
  publication_date: '2024-06-10T12:33:08+00:00'
  date_source: arxiv
- id: hyun2024adversarial
  title: Adversarial Generation of Hierarchical Gaussians for 3d Generative Model
  authors: Sangeek Hyun, Jae-Pil Heo
  year: '2024'
  abstract: Most advances in 3D Generative Adversarial Networks (3D GANs) largely
    depend on ray casting-based volume rendering, which incurs demanding rendering
    costs. One promising alternative is rasterization-based 3D Gaussian Splatting
    (3D-GS), providing a much faster rendering speed and explicit 3D representation.
    In this paper, we exploit Gaussian as a 3D representation for 3D GANs by leveraging
    its efficient and explicit characteristics. However, in an adversarial framework,
    we observe that a naïve generator architecture suffers from training instability
    and lacks the capability to adjust the scale of Gaussians. This leads to model
    divergence and visual artifacts due to the absence of proper guidance for initialized
    positions of Gaussians and densification to manage their scales adaptively. To
    address these issues, we introduce a generator architecture with a hierarchical
    multi-scale Gaussian representation that effectively regularizes the position
    and scale of generated Gaussians. Specifically, we design a hierarchy of Gaussians
    where finer-level Gaussians are parameterized by their coarser-level counterparts;
    the position of finer-level Gaussians would be located near their coarser-level
    counterparts, and the scale would monotonically decrease as the level becomes
    finer, modeling both coarse and fine details of the 3D scene. Experimental results
    demonstrate that ours achieves a significantly faster rendering speed (x100) compared
    to state-of-the-art 3D consistent GANs with comparable 3D generation capability.
  project_page: https://hse1032.github.io/gsgan
  paper: https://arxiv.org/pdf/2406.02968
  code: https://github.com/hse1032/GSGAN
  video: null
  tags:
  - Code
  - GAN
  - Project
  thumbnail: assets/thumbnails/hyun2024adversarial.jpg
  publication_date: '2024-06-05T05:52:20+00:00'
- id: zhang2024gs2mesh
  title: ' RaDe-GS: Rasterizing Depth in Gaussian Splatting '
  authors: Baowen Zhang, Chuan Fang, Rakesh Shrestha, Yixun Liang, Xiaoxiao Long,
    Ping Tan
  year: '2024'
  abstract: Gaussian Splatting (GS) has proven to be highly effective in novel view
    synthesis, achieving high-quality and real-time rendering. However, its potential
    for reconstructing detailed 3D shapes has not been fully explored. Existing methods
    often suffer from limited shape accuracy due to the discrete and unstructured
    nature of Gaussian splats, which complicates the shape extraction. While recent
    techniques like 2D GS have attempted to improve shape reconstruction, they often
    reformulate the Gaussian primitives in ways that reduce both rendering quality
    and computational efficiency. To address these problems, our work introduces a
    rasterized approach to render the depth maps and surface normal maps of general
    3D Gaussian splats. Our method not only significantly enhances shape reconstruction
    accuracy but also maintains the computational efficiency intrinsic to Gaussian
    Splatting. Our approach achieves a Chamfer distance error comparable to NeuraLangelo[Li
    et al. 2023] on the DTU dataset and similar training and rendering time as traditional
    Gaussian Splatting on the Tanks & Temples dataset. Our method is a significant
    advancement in Gaussian Splatting and can be directly integrated into existing
    Gaussian Splatting-based methods.
  project_page: https://baowenz.github.io/radegs/
  paper: https://arxiv.org/pdf/2406.01467
  code: https://github.com/BaowenZ/RaDe-GS
  video: null
  tags:
  - Code
  - Meshing
  - Project
  thumbnail: assets/thumbnails/zhang2024gs2mesh.jpg
  publication_date: '2024-06-03T15:56:58+00:00'
- id: liu2024modgs
  title: 'MoDGS: Dynamic Gaussian Splatting from Causually-captured Monocular Videos'
  authors: Qingming Liu*, Yuan Liu*, Jiepeng Wang, Xianqiang Lv,Peng Wang, Wenping
    Wang, Junhui Hou†,
  year: '2024'
  abstract: In this paper, we propose MoDGS, a new pipeline to render novel-view images
    in dynamic scenes using only casually captured monocular videos. Previous monocular
    dynamic NeRF or Gaussian Splatting methods strongly rely on the rapid movement
    of input cameras to construct multiview consistency but fail to reconstruct dynamic
    scenes on casually captured input videos whose cameras are static or move slowly.
    To address this challenging task, MoDGS adopts recent single-view depth estimation
    methods to guide the learning of the dynamic scene. Then, a novel 3D-aware initialization
    method is proposed to learn a reasonable deformation field and a new robust depth
    loss is proposed to guide the learning of dynamic scene geometry. Comprehensive
    experiments demonstrate that MoDGS is able to render high-quality novel view images
    of dynamic scenes from just a casually captured monocular video, which outperforms
    baseline methods by a significant margin.
  project_page: https://modgs.github.io/
  paper: https://arxiv.org/pdf/2406.00434
  code: null
  video: null
  tags:
  - Dynamic
  - Monocular
  - Project
  thumbnail: assets/thumbnails/liu2024modgs.jpg
  publication_date: '2024-06-01T13:20:46+00:00'
- id: wang2024contextgs
  title: 'ContextGS: Compact 3D Gaussian Splatting with Anchor Level Context Model'
  authors: Yufei Wang, Zhihao Li, Lanqing Guo, Wenhan Yang, Alex C. Kot, Bihan Wen
  year: '2024'
  abstract: 'Recently, 3D Gaussian Splatting (3DGS) has become a promising framework
    for novel view synthesis, offering fast rendering speeds and high fidelity. However,
    the large number of Gaussians and their associated attributes require effective
    compression techniques. Existing methods primarily compress neural Gaussians individually
    and independently, i.e., coding all the neural Gaussians at the same time, with
    little design for their interactions and spatial dependence. Inspired by the effectiveness
    of the context model in image compression, we propose the first autoregressive
    model at the anchor level for 3DGS compression in this work. We divide anchors
    into different levels and the anchors that are not coded yet can be predicted
    based on the already coded ones in all the coarser levels, leading to more accurate
    modeling and higher coding efficiency. To further improve the efficiency of entropy
    coding, e.g., to code the coarsest level with no already coded anchors, we propose
    to introduce a low-dimensional quantized feature as the hyperprior for each anchor,
    which can be effectively compressed. Our work pioneers the context model in the
    anchor level for 3DGS representation, yielding an impressive size reduction of
    over 100 times compared to vanilla 3DGS and 15 times compared to the most recent
    state-of-the-art work Scaffold-GS, while achieving comparable or even higher rendering
    quality.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2405.20721.pdf
  code: https://github.com/wyf0912/ContextGS
  video: null
  tags:
  - Code
  - Compression
  thumbnail: assets/thumbnails/wang2024contextgs.jpg
  publication_date: '2024-05-31T09:23:39+00:00'
  date_source: arxiv
- id: labe2024dgd
  title: 'DGD: Dynamic 3D Gaussians Distillation'
  authors: Isaac Labe*, Noam Issachar*, Itai Lang, Sagie Benaim
  year: '2024'
  abstract: We tackle the task of learning dynamic 3D semantic radiance fields given
    a single monocular video as input. Our learned semantic radiance field captures
    per-point semantics as well as color and geometric properties for a dynamic 3D
    scene, enabling the generation of novel views and their corresponding semantics.
    This enables the segmentation and tracking of a diverse set of 3D semantic entities,
    specified using a simple and intuitive interface that includes a user click or
    a text prompt. To this end, we present DGD, a unified 3D representation for both
    the appearance and semantics of a dynamic 3D scene, building upon the recently
    proposed dynamic 3D Gaussians representation. Our representation is optimized
    over time with both color and semantic information. Key to our method is the joint
    optimization of the appearance and semantic attributes, which jointly affect the
    geometric properties of the scene. We evaluate our approach in its ability to
    enable dense semantic 3D object tracking and demonstrate high-quality results
    that are fast to render, for a diverse set of scenes.
  project_page: https://isaaclabe.github.io/DGD-Website/
  paper: https://arxiv.org/pdf/2405.19321
  code: https://github.com/Isaaclabe/DGD-Dynamic-3D-Gaussians-Distillation
  video: https://www.youtube.com/watch?v=GzX2GJn9OKs
  tags:
  - Code
  - Dynamic
  - Project
  - Video
  thumbnail: assets/thumbnails/labe2024dgd.jpg
  publication_date: '2024-05-29T17:52:22+00:00'
- id: jurca2024rtgs2
  title: 'RT-GS2: Real-Time Generalizable Semantic Segmentation for 3D Gaussian Representations
    of Radiance Fields'
  authors: Mihnea-Bogdan Jurca, Remco Royen, Ion Giosan, Adrian Munteanu
  year: '2024'
  abstract: 'Gaussian Splatting has revolutionized the world of novel view synthesis
    by achieving high rendering performance in real-time. Recently, studies have focused
    on enriching these 3D representations with semantic information for downstream
    tasks. In this paper, we introduce RT-GS2, the first generalizable semantic segmentation
    method employing Gaussian Splatting. While existing Gaussian Splatting-based approaches
    rely on scene-specific training, RT-GS2 demonstrates the ability to generalize
    to unseen scenes. Our method adopts a new approach by first extracting view-independent
    3D Gaussian features in a self-supervised manner, followed by a novel View-Dependent
    / View-Independent (VDVI) feature fusion to enhance semantic consistency over
    different views. Extensive experimentation on three different datasets showcases
    RT-GS2''s superiority over the state-of-the-art methods in semantic segmentation
    quality, exemplified by a 8.01% increase in mIoU on the Replica dataset. Moreover,
    our method achieves real-time performance of 27.03 FPS, marking an astonishing
    901 times speedup compared to existing approaches. This work represents a significant
    advancement in the field by introducing, to the best of our knowledge, the first
    real-time generalizable semantic segmentation method for 3D Gaussian representations
    of radiance fields.

    '
  project_page: https://mbjurca.github.io/rt-gs2/
  paper: https://arxiv.org/pdf/2405.18033.pdf
  code: https://github.com/mbjurca/RT_GS2
  video: null
  tags:
  - Code
  - Point Cloud
  - Project
  - Segmentation
  - Transformer
  - Virtual Reality
  thumbnail: assets/thumbnails/jurca2024rtgs2.jpg
  publication_date: '2024-05-28T10:34:28+00:00'
  date_source: arxiv
- id: wang2024vidu4d
  title: 'Vidu4D: Single Generated Video to High-Fidelity 4D Reconstruction with Dynamic
    Gaussian Surfels'
  authors: Yikai Wang, Xinzhou Wang, Zilong Chen, Zhengyi Wang, Fuchun Sun, Jun Zhu
  year: '2024'
  abstract: 'Video generative models are receiving particular attention given their
    ability to generate realistic and imaginative frames. Besides, these models are
    also observed to exhibit strong 3D consistency, significantly enhancing their
    potential to act as world simulators. In this work, we present Vidu4D, a novel
    reconstruction model that excels in accurately reconstructing 4D (i.e., sequential
    3D) representations from single generated videos, addressing challenges associated
    with non-rigidity and frame distortion. This capability is pivotal for creating
    high-fidelity virtual contents that maintain both spatial and temporal coherence.
    At the core of Vidu4D is our proposed Dynamic Gaussian Surfels (DGS) technique.
    DGS optimizes time-varying warping functions to transform Gaussian surfels (surface
    elements) from a static state to a dynamically warped state. This transformation
    enables a precise depiction of motion and deformation over time. To preserve the
    structural integrity of surface-aligned Gaussian surfels, we design the warped-state
    geometric regularization based on continuous warping fields for estimating normals.
    Additionally, we learn refinements on rotation and scaling parameters of Gaussian
    surfels, which greatly alleviates texture flickering during the warping process
    and enhances the capture of fine-grained appearance details. Vidu4D also contains
    a novel initialization state that provides a proper start for the warping fields
    in DGS. Equipping Vidu4D with an existing video generative model, the overall
    framework demonstrates high-fidelity text-to-4D generation in both appearance
    and geometry.

    '
  project_page: https://vidu4d-dgs.github.io/
  paper: https://arxiv.org/pdf/2405.16822.pdf
  code: https://github.com/yikaiw/vidu4d
  video: null
  tags:
  - 2DGS
  - Code
  - Dynamic
  - Project
  thumbnail: assets/thumbnails/wang2024vidu4d.jpg
  publication_date: '2024-05-27T04:43:44+00:00'
  date_source: arxiv
- id: paul2024spsup2sup360
  title: 'Sp<sup>2</sup>360: Sparse-view 360 Scene Reconstruction using Cascaded 2D
    Diffusion Priors'
  authors: Soumava Paul, Christopher Wewer, Bernt Schiele, Jan Eric Lenssen
  year: '2024'
  abstract: We aim to tackle sparse-view reconstruction of a 360 3D scene using priors
    from latent diffusion models (LDM). The sparse-view setting is ill-posed and underconstrained,
    especially for scenes where the camera rotates 360 degrees around a point, as
    no visual information is available beyond some frontal views focused on the central
    object(s) of interest. In this work, we show that pretrained 2D diffusion models
    can strongly improve the reconstruction of a scene with low-cost fine-tuning.
    Specifically, we present SparseSplat360 (Sp2360), a method that employs a cascade
    of in-painting and artifact removal models to fill in missing details and clean
    novel views. Due to superior training and rendering speeds, we use an explicit
    scene representation in the form of 3D Gaussians over NeRF-based implicit representations.
    We propose an iterative update strategy to fuse generated pseudo novel views with
    existing 3D Gaussians fitted to the initial sparse inputs. As a result, we obtain
    a multi-view consistent scene representation with details coherent with the observed
    inputs. Our evaluation on the challenging Mip-NeRF360 dataset shows that our proposed
    2D to 3D distillation algorithm considerably improves the performance of a regularized
    version of 3DGS adapted to a sparse-view setting and outperforms existing sparse-view
    reconstruction methods in 360 scene reconstruction. Qualitatively, our method
    generates entire 360 scenes from as few as 9 input views, with a high degree of
    foreground and background detail.
  project_page: null
  paper: https://arxiv.org/pdf/2405.16517
  code: https://github.com/mvp18/sp2-360
  video: null
  tags:
  - Code
  - Sparse
  thumbnail: assets/thumbnails/paul2024spsup2sup360.jpg
  publication_date: '2024-05-26T11:01:39+00:00'
- id: condor2024dsyg
  title: 'Don''t Splat your Gaussians: Volumetric Ray-Traced Primitives for Modeling
    and Rendering Scattering and Emissive Media'
  authors: Jorge Condor, Sebastien Speierer, Lukas Bode, Aljaz Bozic, Simon Green,
    Piotr Didyk, Adrian Jarabo
  year: '2024'
  abstract: 'Banking on the popularity of rasterized 3D Gaussian Splatting methods,
    we formalize the ray-tracing of volumes composed of kernel mixture models (Gaussian
    or otherwise). Our physically-based, path-traced formulation allows us to render
    and optimize both scattering and emissive volumes, as well as radiance fields,
    in an extremely efficient and compact manner. We also introduce the Epanechnikov
    kernel as an efficient alternative for the Gaussian kernel in radiance field rendering,
    and showcase the advantages of a ray-traced framework, while maintaining real-time
    performance. '
  project_page: https://arcanous98.github.io/projectPages/gaussianVolumes.html
  paper: https://arcanous98.github.io/assets/data/papers/Gaussian_tracing_meta_TOG-compressed.pdf
  code: https://github.com/facebookresearch/volumetric_primitives
  video: null
  tags:
  - Physics
  - Ray Tracing
  - Relight
  - Rendering
  - Project
  - Code
  - 360 degree
  - Antialiasing
  - Perspective-correct
  thumbnail: assets/thumbnails/condor2024dsyg.jpg
  publication_date: '2024-05-24T10:42:05+00:00'
  date_source: arxiv
- id: chen2024dogs
  title: 'DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction
    Via Gaussian Consensus'
  authors: Yu Chen, Gim Hee Lee
  year: '2024'
  abstract: 'The recent advances in 3D Gaussian Splatting (3DGS) show promising results
    on the novel view synthesis (NVS) task. With its superior rendering performance
    and high-fidelity rendering quality, 3DGS is excelling at its previous NeRF counterparts.
    The most recent 3DGS method focuses either on improving the instability of rendering
    efficiency or reducing the model size. On the other hand, the training efficiency
    of 3DGS on large-scale scenes has not gained much attention. In this work, we
    propose DoGaussian, a method that trains 3DGS distributedly. Our method first
    decomposes a scene into K blocks and then introduces the Alternating Direction
    Method of Multipliers (ADMM) into the training procedure of 3DGS. During training,
    our DOGS maintains one global 3DGS model on the master node and K local 3DGS models
    on the slave nodes. The K local 3DGS models are dropped after training and we
    only query the global 3DGS model during inference. The training time is reduced
    by scene decomposition, and the training convergence and stability are guaranteed
    through the consensus on the shared 3D Gaussians. Our method accelerates the training
    of 3DGS by 6+ times when evaluated on large-scale scenes while concurrently achieving
    state-of-the-art rendering quality. Our code is publicly available at https://github.com/AIBluefisher/DOGS.

    '
  project_page: https://aibluefisher.github.io/DOGS/
  paper: https://arxiv.org/pdf/2405.13943.pdf
  code: https://github.com/AIBluefisher/DOGS
  video: null
  tags:
  - Code
  - Large-Scale
  - Project
  thumbnail: assets/thumbnails/chen2024dogs.jpg
  publication_date: '2024-05-22T19:17:58+00:00'
  date_source: arxiv
- id: li2024garmentdreamer
  title: 'GarmentDreamer: 3DGS Guided Garment Synthesis with Diverse Geometry and
    Texture Details'
  authors: Boqian Li, Xuan Li, Ying Jiang, Tianyi Xie, Feng Gao, Huamin Wang, Yin
    Yang, Chenfanfu Jiang
  year: '2024'
  abstract: 'Traditional 3D garment creation is labor-intensive, involving sketching,
    modeling, UV mapping, and texturing, which are time-consuming and costly. Recent
    advances in diffusion-based generative models have enabled new possibilities for
    3D garment generation from text prompts, images, and videos. However, existing
    methods either suffer from inconsistencies among multi-view images or require
    additional processes to separate cloth from the underlying human model. In this
    paper, we propose GarmentDreamer, a novel method that leverages 3D Gaussian Splatting
    (GS) as guidance to generate wearable, simulation-ready 3D garment meshes from
    text prompts. In contrast to using multi-view images directly predicted by generative
    models as guidance, our 3DGS guidance ensures consistent optimization in both
    garment deformation and texture synthesis. Our method introduces a novel garment
    augmentation module, guided by normal and RGBA information, and employs implicit
    Neural Texture Fields (NeTF) combined with Score Distillation Sampling (SDS) to
    generate diverse geometric and texture details. We validate the effectiveness
    of our approach through comprehensive qualitative and quantitative experiments,
    showcasing the superior performance of GarmentDreamer over state-of-the-art alternatives.
    Our project page is available at: https://xuan-li.github.io/GarmentDreamerDemo/.

    '
  project_page: https://xuan-li.github.io/GarmentDreamerDemo/
  paper: https://arxiv.org/pdf/2405.12420.pdf
  code: https://github.com/boqian-li/GarmentDreamer
  video: https://xuan-li.github.io/GarmentDreamerDemo/dance.mp4
  tags:
  - Avatar
  - Code
  - Dynamic
  - Project
  - Rendering
  - Texturing
  - Video
  thumbnail: assets/thumbnails/li2024garmentdreamer.jpg
  publication_date: '2024-05-20T23:54:28+00:00'
  date_source: arxiv
- id: wu2024gaussian
  title: 'Gaussian Head & Shoulders: High Fidelity Neural Upper Body Avatars with
    Anchor Gaussian Guided Texture Warping'
  authors: Tianhao Wu, Jing Yang, Zhilin Guo, Jingyi Wan, Fangcheng Zhong, Cengiz
    Oztireli
  year: '2024'
  abstract: 'By equipping the most recent 3D Gaussian Splatting representation with
    head 3D morphable models (3DMM), existing methods manage to create head avatars
    with high fidelity. However, most existing methods only reconstruct a head without
    the body, substantially limiting their application scenarios. We found that naively
    applying Gaussians to model the clothed chest and shoulders tends to result in
    blurry reconstruction and noisy floaters under novel poses. This is because of
    the fundamental limitation of Gaussians and point clouds -- each Gaussian or point
    can only have a single directional radiance without spatial variance, therefore
    an unnecessarily large number of them is required to represent complicated spatially
    varying texture, even for simple geometry. In contrast, we propose to model the
    body part with a neural texture that consists of coarse and pose-dependent fine
    colors. To properly render the body texture for each view and pose without accurate
    geometry nor UV mapping, we optimize another sparse set of Gaussians as anchors
    that constrain the neural warping field that maps image plane coordinates to the
    texture space. We demonstrate that Gaussian Head & Shoulders can fit the high-frequency
    details on the clothed upper body with high fidelity and potentially improve the
    accuracy and fidelity of the head region. We evaluate our method with casual phone-captured
    and internet videos and show our method archives superior reconstruction quality
    and robustness in both self and cross reenactment tasks. To fully utilize the
    efficient rendering speed of Gaussian splatting, we additionally propose an accelerated
    inference method of our trained model without Multi-Layer Perceptron (MLP) queries
    and reach a stable rendering speed of around 130 FPS for any subjects.

    '
  project_page: https://gaussian-head-shoulders.netlify.app/
  paper: https://arxiv.org/pdf/2405.12069.pdf
  code: null
  video: null
  tags:
  - Avatar
  - Dynamic
  - Project
  thumbnail: assets/thumbnails/wu2024gaussian.jpg
  publication_date: '2024-05-20T14:39:49+00:00'
  date_source: arxiv
- id: dalal2024gaussian
  title: 'Gaussian Splatting: 3D Reconstruction and Novel View Synthesis, a Review'
  authors: Anurag Dalal, Daniel Hagen, Kjell G. Robbersmyr, Kristian Muri Knausgård
  year: '2024'
  abstract: Image-based 3D reconstruction is a challenging task that involves inferring
    the 3D shape of an object or scene from a set of input images. Learning-based
    methods have gained attention for their ability to directly estimate 3D shapes.
    This review paper focuses on state-of-the-art techniques for 3D reconstruction,
    including the generation of novel, unseen views. An overview of recent developments
    in the Gaussian Splatting method is provided, covering input types, model structures,
    output representations, and training strategies. Unresolved challenges and future
    directions are also discussed. Given the rapid progress in this domain and the
    numerous opportunities for enhancing 3D reconstruction methods, a comprehensive
    examination of algorithms appears essential. Consequently, this study offers a
    thorough overview of the latest advancements in Gaussian Splatting.
  project_page: null
  paper: https://arxiv.org/pdf/2405.03417
  code: null
  video: null
  tags:
  - Review
  thumbnail: assets/thumbnails/dalal2024gaussian.jpg
  publication_date: '2024-05-06T12:32:38+00:00'
- id: chu2024dreamscene4d
  title: 'DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular Videos'
  authors: Wen-Hsuan Chu, Lei Ke, Katerina Fragkiadaki
  year: '2024'
  abstract: Existing VLMs can track in-the-wild 2D video objects while current generative
    models provide powerful visual priors for synthesizing novel views for the highly
    under-constrained 2D-to-3D object lifting. Building upon this exciting progress,
    we present DreamScene4D, the first approach that can generate three-dimensional
    dynamic scenes of multiple objects from monocular in-the-wild videos with large
    object motion across occlusions and novel viewpoints. Our key insight is to design
    a "decompose-then-recompose" scheme to factorize both the whole video scene and
    each object's 3D motion. We first decompose the video scene by using open-vocabulary
    mask trackers and an adapted image diffusion model to segment, track, and amodally
    complete the objects and background in the video. Each object track is mapped
    to a set of 3D Gaussians that deform and move in space and time. We also factorize
    the observed motion into multiple components to handle fast motion. The camera
    motion can be inferred by re-rendering the background to match the video frames.
    For the object motion, we first model the object-centric deformation of the objects
    by leveraging rendering losses and multi-view generative priors in an object-centric
    frame, then optimize object-centric to world-frame transformations by comparing
    the rendered outputs against the perceived pixel and optical flow. Finally, we
    recompose the background and objects and optimize for relative object scales using
    monocular depth prediction guidance. We show extensive results on the challenging
    DAVIS, Kubric, and self-captured videos, detail some limitations, and provide
    future directions. Besides 4D scene generation, our results show that DreamScene4D
    enables accurate 2D point motion tracking by projecting the inferred 3D trajectories
    to 2D, while never explicitly trained to do so.
  project_page: https://dreamscene4d.github.io/
  paper: https://arxiv.org/pdf/2405.02280
  code: https://github.com/dreamscene4d/dreamscene4d
  video: null
  tags:
  - Code
  - Dynamic
  - Monocular
  - Project
  thumbnail: assets/thumbnails/chu2024dreamscene4d.jpg
  publication_date: '2024-05-03T17:55:34+00:00'
- id: peng2024rtgslam
  title: 'RTG-SLAM: Real-time 3D Reconstruction at Scale using Gaussian Splatting'
  authors: Zhexi Peng, Tianjia Shao, Yong Liu, Jingke Zhou, Yin Yang, Jingdong Wang,
    Kun Zhou
  year: '2024'
  abstract: 'We present Real-time Gaussian SLAM (RTG-SLAM), a real-time 3D reconstruction
    system with an RGBD camera for large-scale environments using Gaussian splatting.
    The system features a compact Gaussian representation and a highly efficient on-the-fly
    Gaussian optimization scheme. We force each Gaussian to be either opaque or nearly
    transparent, with the opaque ones fitting the surface and dominant colors, and
    transparent ones fitting residual colors. By rendering depth in a different way
    from color rendering, we let a single opaque Gaussian well fit a local surface
    region without the need of multiple overlapping Gaussians, hence largely reducing
    the memory and computation cost. For on-the-fly Gaussian optimization, we explicitly
    add Gaussians for three types of pixels per frame: newly observed, with large
    color errors, and with large depth errors. We also categorize all Gaussians into
    stable and unstable ones, where the stable Gaussians are expected to well fit
    previously observed RGBD images and otherwise unstable. We only optimize the unstable
    Gaussians and only render the pixels occupied by unstable Gaussians. In this way,
    both the number of Gaussians to be optimized and pixels to be rendered are largely
    reduced, and the optimization can be done in real time. We show real-time reconstructions
    of a variety of large scenes. Compared with the state-of-the-art NeRF-based RGBD
    SLAM, our system achieves comparable high-quality reconstruction but with around
    twice the speed and half the memory cost, and shows superior performance in the
    realism of novel view synthesis and camera tracking accuracy.'
  project_page: https://gapszju.github.io/RTG-SLAM/
  paper: https://arxiv.org/pdf/2404.19706
  code: https://github.com/MisEty/RTG-SLAM
  video: null
  tags:
  - Code
  - Project
  - SLAM
  thumbnail: assets/thumbnails/peng2024rtgslam.jpg
  publication_date: '2024-04-30T16:54:59+00:00'
- id: ni2024phyrecon
  title: 'PhyRecon: Physically Plausible Neural Scene Reconstruction'
  authors: Junfeng Ni, Yixin Chen, Bohan Jing, Nan Jiang, Bin Wang, Bo Dai, Puhao
    Li, Yixin Zhu, Song-Chun Zhu, Siyuan Huang
  year: '2024'
  abstract: 'We address the issue of physical implausibility in multi-view neural
    reconstruction. While implicit representations have gained popularity in multi-view
    3D reconstruction, previous work struggles to yield physically plausible results,
    limiting their utility in domains requiring rigorous physical accuracy. This lack
    of plausibility stems from the absence of physics modeling in existing methods
    and their inability to recover intricate geometrical structures. In this paper,
    we introduce PHYRECON, the first approach to leverage both differentiable rendering
    and differentiable physics simulation to learn implicit surface representations.
    PHYRECON features a novel differentiable particle-based physical simulator built
    on neural implicit representations. Central to this design is an efficient transformation
    between SDF-based implicit representations and explicit surface points via our
    proposed Surface Points Marching Cubes (SP-MC), enabling differentiable learning
    with both rendering and physical losses. Additionally, PHYRECON models both rendering
    and physical uncertainty to identify and compensate for inconsistent and inaccurate
    monocular geometric priors. The physical uncertainty further facilitates physics-guided
    pixel sampling to enhance the learning of slender structures. By integrating these
    techniques, our model supports differentiable joint modeling of appearance, geometry,
    and physics. Extensive experiments demonstrate that PHYRECON significantly improves
    the reconstruction quality. Our results also exhibit superior physical stability
    in physical simulators, with at least a 40% improvement across all datasets, paving
    the way for future physics-based applications.

    '
  project_page: https://phyrecon.github.io/
  paper: https://arxiv.org/pdf/2404.16666.pdf
  code: https://github.com/PhyRecon/PhyRecon
  video: https://www.youtube.com/watch?v=ZXe3bGJiL_k
  tags:
  - Code
  - Dynamic
  - Meshing
  - Physics
  - Project
  - Video
  thumbnail: assets/thumbnails/ni2024phyrecon.jpg
  publication_date: '2024-04-25T15:06:58+00:00'
  date_source: arxiv
- id: lee2024guess
  title: 'Guess The Unseen: Dynamic 3D Scene Reconstruction from Partial 2D Glimpses'
  authors: Inhee Lee, Byungjun Kim, Hanbyul Joo
  year: '2024'
  abstract: In this paper, we present a method to reconstruct the world and multiple
    dynamic humans in 3D from a monocular video input. As a key idea, we represent
    both the world and multiple humans via the recently emerging 3D Gaussian Splatting
    (3D-GS) representation, enabling to conveniently and efficiently compose and render
    them together. In particular, we address the scenarios with severely limited and
    sparse observations in 3D human reconstruction, a common challenge encountered
    in the real world. To tackle this challenge, we introduce a novel approach to
    optimize the 3D-GS representation in a canonical space by fusing the sparse cues
    in the common space, where we leverage a pre-trained 2D diffusion model to synthesize
    unseen views while keeping the consistency with the observed 2D appearances. We
    demonstrate our method can reconstruct high-quality animatable 3D humans in various
    challenging examples, in the presence of occlusion, image crops, few-shot, and
    extremely sparse observations. After reconstruction, our method is capable of
    not only rendering the scene in any novel views at arbitrary time instances, but
    also editing the 3D scene by removing individual humans or applying different
    motions for each human. Through various experiments, we demonstrate the quality
    and efficiency of our methods over alternative existing approaches.
  project_page: https://snuvclab.github.io/gtu/
  paper: https://arxiv.org/pdf/2404.14410
  code: https://github.com/snuvclab/gtu/
  video: https://youtu.be/l9c_rd4hmFI
  tags:
  - Avatar
  - Code
  - Project
  - Video
  thumbnail: assets/thumbnails/lee2024guess.jpg
  publication_date: '2024-04-22T17:59:50+00:00'
- id: c2024contrastive
  title: 'Contrastive Gaussian Clustering: Weakly Supervised 3D Scene Segmentation'
  authors: Myrna C. Silva, Mahtab Dahaghin, Matteo Toso, Alessio Del Bue
  year: '2024'
  abstract: We introduce Contrastive Gaussian Clustering, a novel approach capable
    of provide segmentation masks from any viewpoint and of enabling 3D segmentation
    of the scene. Recent works in novel-view synthesis have shown how to model the
    appearance of a scene via a cloud of 3D Gaussians, and how to generate accurate
    images from a given viewpoint by projecting on it the Gaussians before α blending
    their color. Following this example, we train a model to include also a segmentation
    feature vector for each Gaussian. These can then be used for 3D scene segmentation,
    by clustering Gaussians according to their feature vectors; and to generate 2D
    segmentation masks, by projecting the Gaussians on a plane and α blending over
    their segmentation features. Using a combination of contrastive learning and spatial
    regularization, our method can be trained on inconsistent 2D segmentation masks,
    and still learn to generate segmentation masks consistent across all views. Moreover,
    the resulting model is extremely accurate, improving the IoU accuracy of the predicted
    masks by +8% over the state of the art. Code and trained models will be released
    upon acceptance.
  project_page: null
  paper: https://arxiv.org/pdf/2404.12784
  code: null
  video: null
  tags:
  - Segmentation
  thumbnail: assets/thumbnails/c2024contrastive.jpg
  publication_date: '2024-04-19T10:47:53+00:00'
- id: liu2024infusion
  title: 'InFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion
    Prior'
  authors: Zhiheng Liu, Hao Ouyang, Qiuyu Wang, Ka Leong Cheng, Jie Xiao, Kai Zhu,
    Nan Xue, Yu Liu, Yujun Shen, Yang Cao
  year: '2024'
  abstract: 3D Gaussians have recently emerged as an efficient representation for
    novel view synthesis. This work studies its editability with a particular focus
    on the inpainting task, which aims to supplement an incomplete set of 3D Gaussians
    with additional points for visually harmonious rendering. Compared to 2D inpainting,
    the crux of inpainting 3D Gaussians is to figure out the rendering-relevant properties
    of the introduced points, whose optimization largely benefits from their initial
    3D positions. To this end, we propose to guide the point initialization with an
    image-conditioned depth completion model, which learns to directly restore the
    depth map based on the observed image. Such a design allows our model to fill
    in depth values at an aligned scale with the original depth, and also to harness
    strong generalizability from largescale diffusion prior. Thanks to the more accurate
    depth completion, our approach, dubbed InFusion, surpasses existing alternatives
    with sufficiently better fidelity and efficiency under various complex scenarios.
    We further demonstrate the effectiveness of InFusion with several practical applications,
    such as inpainting with user-specific texture or with novel object insertion.
  project_page: https://johanan528.github.io/Infusion/
  paper: https://arxiv.org/pdf/2404.11613
  code: https://github.com/ali-vilab/infusion
  video: null
  tags:
  - Code
  - Editing
  - Inpainting
  - Project
  thumbnail: assets/thumbnails/liu2024infusion.jpg
  publication_date: '2024-04-17T17:59:53+00:00'
- id: oh2024deblurgs
  title: 'DeblurGS: Gaussian Splatting for Camera Motion Blur'
  authors: Jeongtaek Oh, Jaeyoung Chung, Dongwoo Lee, Kyoung Mu Lee
  year: '2024'
  abstract: Although significant progress has been made in reconstructing sharp 3D
    scenes from motion-blurred images, a transition to realworld applications remains
    challenging. The primary obstacle stems from the severe blur which leads to inaccuracies
    in the acquisition of initial camera poses through Structure-from-Motion, a critical
    aspect often overlooked by previous approaches. To address this challenge, we
    propose DeblurGS, a method to optimize sharp 3D Gaussian Splatting from motion-blurred
    images, even with the noisy camera pose initialization. We restore a fine-grained
    sharp scene by leveraging the remarkable reconstruction capability of 3D Gaussian
    Splatting. Our approach estimates the 6-Degree-of-Freedom camera motion for each
    blurry observation and synthesizes corresponding blurry renderings for the optimization
    process. Furthermore, we propose Gaussian Densification Annealing strategy to
    prevent the generation of inaccurate Gaussians at erroneous locations during the
    early training stages when camera motion is still imprecise. Comprehensive experiments
    demonstrate that our DeblurGS achieves state-of-the-art performance in deblurring
    and novel view synthesis for real-world and synthetic benchmark datasets, as well
    as field-captured blurry smartphone videos.
  project_page: null
  paper: https://arxiv.org/pdf/2404.11358
  code: null
  video: null
  tags:
  - Deblurring
  - Rendering
  thumbnail: assets/thumbnails/oh2024deblurgs.jpg
  publication_date: '2024-04-17T13:14:52+00:00'
- id: barthel2024gaussian
  title: Gaussian Splatting Decoder for 3D‑aware Generative Adversarial Networks
  authors: Florian Barthel, Arian Beckmann, Wieland Morgenstern, Anna Hilsmann, Peter
    Eisert
  year: '2024'
  abstract: 'NeRF-based 3D-aware Generative Adversarial Networks like EG3D or GIRAFFE
    have shown very high rendering quality under large representational variety. However,
    rendering with Neural Radiance Fields poses several challenges for most 3D applications:
    First, the significant computational demands of NeRF rendering preclude its use
    on low-power devices, such as mobiles and VR/AR headsets. Second, implicit representations
    based on neural networks are difficult to incorporate into explicit 3D scenes,
    such as VR environments or video games. 3D Gaussian Splatting (3DGS) overcomes
    these limitations by providing an explicit 3D representation that can be rendered
    efficiently at high frame rates. In this work, we present a novel approach that
    combines the high rendering quality of NeRF-based 3D-aware Generative Adversarial
    Networks with the flexibility and computational advantages of 3DGS. By training
    a decoder that maps implicit NeRF representations to explicit 3D Gaussian Splatting
    attributes, we can integrate the representational diversity and quality of 3D
    GANs into the ecosystem of 3D Gaussian Splatting for the first time. Additionally,
    our approach allows for a high resolution GAN inversion and real-time GAN editing
    with 3D Gaussian Splatting scenes.'
  project_page: https://florian-barthel.github.io/gaussian_decoder/index.html
  paper: https://arxiv.org/pdf/2404.10625
  code: https://github.com/fraunhoferhhi/gaussian_gan_decoder
  video: https://florian-barthel.github.io/gaussian_decoder/videos/latent.mp4
  tags:
  - Avatar
  - Code
  - Project
  - Video
  thumbnail: assets/thumbnails/barthel2024gaussian.jpg
  publication_date: '2024-04-16T14:48:40+00:00'
- id: cui2024letsgo
  title: 'LetsGo: Large-Scale Garage Modeling and Rendering via LiDAR-Assisted Gaussian
    Primitives'
  authors: Jiadi Cui, Junming Cao, Fuqiang Zhao, Zhipeng He, Yifan Chen, Yuhui Zhong,
    Lan Xu, Yujiao Shi, Yingliang Zhang, Jingyi Yu
  year: '2024'
  abstract: 'Large garages are ubiquitous yet intricate scenes that present unique
    challenges due to their monotonous colors, repetitive patterns, reflective surfaces,
    and transparent vehicle glass. Conventional Structure from Motion (SfM) methods
    for camera pose estimation and 3D reconstruction often fail in these environments
    due to poor correspondence construction. To address these challenges, we introduce
    LetsGo, a LiDAR-assisted Gaussian splatting framework for large-scale garage modeling
    and rendering. We develop a handheld scanner, Polar, equipped with IMU, LiDAR,
    and a fisheye camera, to facilitate accurate data acquisition. Using this Polar
    device, we present the GarageWorld dataset, consisting of eight expansive garage
    scenes with diverse geometric structures, which will be made publicly available
    for further research. Our approach demonstrates that LiDAR point clouds collected
    by the Polar device significantly enhance a suite of 3D Gaussian splatting algorithms
    for garage scene modeling and rendering. We introduce a novel depth regularizer
    that effectively eliminates floating artifacts in rendered images. Additionally,
    we propose a multi-resolution 3D Gaussian representation designed for Level-of-Detail
    (LOD) rendering. This includes adapted scaling factors for individual levels and
    a random-resolution-level training scheme to optimize the Gaussians across different
    resolutions. This representation enables efficient rendering of large-scale garage
    scenes on lightweight devices via a web-based renderer. Experimental results on
    our GarageWorld dataset, as well as on ScanNet++ and KITTI-360, demonstrate the
    superiority of our method in terms of rendering quality and resource efficiency.

    '
  project_page: https://zhaofuq.github.io/LetsGo/
  paper: https://arxiv.org/pdf/2404.09748.pdf
  code: https://github.com/zhaofuq/LOD-3DGS
  video: https://youtu.be/fs42UBKvGRw?si=v1D0kPj1-QEzpMSR
  tags:
  - Code
  - Large-Scale
  - Lidar
  - Project
  - Video
  thumbnail: assets/thumbnails/cui2024letsgo.jpg
  publication_date: '2024-04-15T12:50:44+00:00'
  date_source: arxiv
- id: kheradmand20243d
  title: 3D Gaussian Splatting as Markov Chain Monte Carlo
  authors: Shakiba Kheradmand, Daniel Rebain, Gopal Sharma, Weiwei Sun, Jeff Tseng,
    Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, Kwang Moo Yi
  year: '2024'
  abstract: 'While 3D Gaussian Splatting has recently become popular for neural rendering,
    current methods rely on carefully engineered cloning and splitting strategies
    for placing Gaussians, which can lead to poor-quality renderings, and reliance
    on a good initialization. In this work, we rethink the set of 3D Gaussians as
    a random sample drawn from an underlying probability distribution describing the
    physical representation of the scene-in other words, Markov Chain Monte Carlo
    (MCMC) samples. Under this view, we show that the 3D Gaussian updates can be converted
    as Stochastic Gradient Langevin Dynamics (SGLD) updates by simply introducing
    noise. We then rewrite the densification and pruning strategies in 3D Gaussian
    Splatting as simply a deterministic state transition of MCMC samples, removing
    these heuristics from the framework. To do so, we revise the ''cloning'' of Gaussians
    into a relocalization scheme that approximately preserves sample probability.
    To encourage efficient use of Gaussians, we introduce a regularizer that promotes
    the removal of unused Gaussians. On various standard evaluation scenes, we show
    that our method provides improved rendering quality, easy control over the number
    of Gaussians, and robustness to initialization.

    '
  project_page: https://ubc-vision.github.io/3dgs-mcmc/
  paper: https://arxiv.org/pdf/2404.09591.pdf
  code: https://github.com/ubc-vision/3dgs-mcmc
  video: null
  tags:
  - Code
  - Densification
  - Project
  thumbnail: assets/thumbnails/kheradmand20243d.jpg
  publication_date: '2024-04-15T09:01:47+00:00'
  date_source: arxiv
- id: li2024loopgaussian
  title: 'LoopGaussian: Creating 3D Cinemagraph with Multi-view Images via Eulerian
    Motion Field'
  authors: Jiyang Li, Lechao Cheng, Zhangye Wang, Tingting Mu, Jingxuan He
  year: '2024'
  abstract: Cinemagraph is a unique form of visual media that combines elements of
    still photography and subtle motion to create a captivating experience. However,
    the majority of videos generated by recent works lack depth information and are
    confined to the constraints of 2D image space. In this paper, inspired by significant
    progress in the field of novel view synthesis (NVS) achieved by 3D Gaussian Splatting
    (3D-GS), we propose LoopGaussian to elevate cinemagraph from 2D image space to
    3D space using 3D Gaussian modeling. To achieve this, we first employ the 3D-GS
    method to reconstruct 3D Gaussian point clouds from multi-view images of static
    scenes,incorporating shape regularization terms to prevent blurring or artifacts
    caused by object deformation. We then adopt an autoencoder tailored for 3D Gaussian
    to project it into feature space. To maintain the local continuity of the scene,
    we devise SuperGaussian for clustering based on the acquired features. By calculating
    the similarity between clusters and employing a two-stage estimation method, we
    derive an Eulerian motion field to describe velocities across the entire scene.
    The 3D Gaussian points then move within the estimated Eulerian motion field. Through
    bidirectional animation techniques, we ultimately generate a 3D Cinemagraph that
    exhibits natural and seamlessly loopable dynamics. Experiment results validate
    the effectiveness of our approach, demonstrating high-quality and visually appealing
    scene generation.
  project_page: https://pokerlishao.github.io/LoopGaussian/
  paper: https://arxiv.org/pdf/2404.08966
  code: https://github.com/Pokerlishao/LoopGaussian
  video: null
  tags:
  - Code
  - Physics
  - Project
  - Rendering
  thumbnail: assets/thumbnails/li2024loopgaussian.jpg
  publication_date: '2024-04-13T11:07:53+00:00'
- id: ye2024occgaussian
  title: 'OccGaussian: 3D Gaussian Splatting for Occluded Human Rendering'
  authors: Jingrui Ye, Zongkai Zhang, Yujiao Jiang, Qingmin Liao, Wenming Yang, Zongqing
    Lu
  year: '2024'
  abstract: Rendering dynamic 3D human from monocular videos is crucial for various
    applications such as virtual reality and digital entertainment. Most methods assume
    the people is in an unobstructed scene, while various objects may cause the occlusion
    of body parts in real-life scenarios. Previous method utilizing NeRF for surface
    rendering to recover the occluded areas, but it requiring more than one day to
    train and several seconds to render, failing to meet the requirements of real-time
    interactive applications. To address these issues, we propose OccGaussian based
    on 3D Gaussian Splatting, which can be trained within 6 minutes and produces high-quality
    human renderings up to 160 FPS with occluded input. OccGaussian initializes 3D
    Gaussian distributions in the canonical space, and we perform occlusion feature
    query at occluded regions, the aggregated pixel-align feature is extracted to
    compensate for the missing information. Then we use Gaussian Feature MLP to further
    process the feature along with the occlusion-aware loss functions to better perceive
    the occluded area. Extensive experiments both in simulated and real-world occlusions,
    demonstrate that our method achieves comparable or even superior performance compared
    to the state-of-the-art method. And we improving training and inference speeds
    by 250x and 800x, respectively.
  project_page: https://wenj.github.io/GoMAvatar/
  paper: https://arxiv.org/pdf/2404.07991
  code: https://github.com/wenj/GoMAvatar
  video: null
  tags:
  - Avatar
  - Code
  - Project
  thumbnail: assets/thumbnails/ye2024occgaussian.jpg
  publication_date: '2024-04-11T17:59:57+00:00'
- id: wen2024gomavatar
  title: 'GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using
    Gaussians-on-Mesh'
  authors: Jing Wen, Xiaoming Zhao, Zhongzheng Ren, Alexander G. Schwing, Shenlong
    Wang
  year: '2024'
  abstract: We introduce GoMAvatar, a novel approach for real-time, memory-efficient,
    high-quality animatable human modeling. GoMAvatar takes as input a single monocular
    video to create a digital avatar capable of re-articulation in new poses and real-time
    rendering from novel viewpoints, while seamlessly integrating with rasterization-based
    graphics pipelines. Central to our method is the Gaussians-on-Mesh representation,
    a hybrid 3D model combining rendering quality and speed of Gaussian splatting
    with geometry modeling and compatibility of deformable meshes. We assess GoMAvatar
    on ZJU-MoCap data and various YouTube videos. GoMAvatar matches or surpasses current
    monocular human modeling algorithms in rendering quality and significantly outperforms
    them in computational efficiency (43 FPS) while being memory-efficient (3.63 MB
    per subject).
  project_page: https://wenj.github.io/GoMAvatar/
  paper: https://arxiv.org/pdf/2404.07991
  code: https://github.com/wenj/GoMAvatar
  video: null
  tags:
  - Avatar
  - Code
  - Monocular
  - Project
  thumbnail: assets/thumbnails/wen2024gomavatar.jpg
  publication_date: '2024-04-11T17:59:57+00:00'
- id: lyu2024gaga
  title: 'Gaga: Group Any Gaussians via 3D-aware Memory Bank'
  authors: Weijie Lyu, Xueting Li, Abhijit Kundu, Yi-Hsuan Tsai, Ming-Hsuan Yang
  year: '2024'
  abstract: We introduce Gaga, a framework that reconstructs and segments open-world
    3D scenes by leveraging inconsistent 2D masks predicted by zero-shot segmentation
    models. Contrasted to prior 3D scene segmentation approaches that heavily rely
    on video object tracking, Gaga utilizes spatial information and effectively associates
    object masks across diverse camera poses. By eliminating the assumption of continuous
    view changes in training images, Gaga demonstrates robustness to variations in
    camera poses, particularly beneficial for sparsely sampled images, ensuring precise
    mask label consistency. Furthermore, Gaga accommodates 2D segmentation masks from
    diverse sources and demonstrates robust performance with different open-world
    zero-shot segmentation models, significantly enhancing its versatility. Extensive
    qualitative and quantitative evaluations demonstrate that Gaga performs favorably
    against state-of-the-art methods, emphasizing its potential for real-world applications
    such as scene understanding and manipulation.
  project_page: https://www.gaga.gallery/
  paper: https://arxiv.org/pdf/2404.07977.pdf
  code: https://github.com/weijielyu/Gaga
  video: https://www.youtube.com/watch?v=rqs5BuVFOok
  tags:
  - Code
  - Editing
  - Project
  - Segmentation
  - Video
  thumbnail: assets/thumbnails/lyu2024gaga.jpg
  publication_date: '2024-04-11T17:57:19+00:00'
- id: shriram2024realmdreamer
  title: 'RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth
    Diffusion'
  authors: Jaidev Shriram, Alex Trevithick, Lingjie Liu, Ravi Ramamoorthi
  year: '2024'
  abstract: We introduce RealmDreamer, a technique for generation of general forward-facing
    3D scenes from text descriptions. Our technique optimizes a 3D Gaussian Splatting
    representation to match complex text prompts. We initialize these splats by utilizing
    the state-of-the-art text-to-image generators, lifting their samples into 3D,
    and computing the occlusion volume. We then optimize this representation across
    multiple views as a 3D inpainting task with image-conditional diffusion models.
    To learn correct geometric structure, we incorporate a depth diffusion model by
    conditioning on the samples from the inpainting model, giving rich geometric structure.
    Finally, we finetune the model using sharpened samples from image generators.
    Notably, our technique does not require video or multi-view data and can synthesize
    a variety of high-quality 3D scenes in different styles, consisting of multiple
    objects. Its generality additionally allows 3D synthesis from a single image
  project_page: https://realmdreamer.github.io/
  paper: https://arxiv.org/pdf/2404.07199
  code: null
  video: null
  tags:
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/shriram2024realmdreamer.jpg
  publication_date: '2024-04-10T17:57:41+00:00'
- id: lang2024gaussianlic
  title: 'Gaussian-LIC: Photo-realistic LiDAR-Inertial-Camera SLAM with 3D Gaussian
    Splatting'
  authors: Xiaolei Lang, Laijian Li, Hang Zhang, Feng Xiong, Mu Xu, Yong Liu, Xingxing
    Zuo, Jiajun Lv
  year: '2024'
  abstract: We present a real-time LiDAR-Inertial-Camera SLAM system with 3D Gaussian
    Splatting as the mapping backend. Leveraging robust pose estimates from our LiDAR-Inertial-Camera
    odometry, Coco-LIC, an incremental photo-realistic mapping system is proposed
    in this paper. We initialize 3D Gaussians from colorized LiDAR points and optimize
    them using differentiable rendering powered by 3D Gaussian Splatting. Meticulously
    designed strategies are employed to incrementally expand the Gaussian map and
    adaptively control its density, ensuring high-quality mapping with real-time capability.
    Experiments conducted in diverse scenarios demonstrate the superior performance
    of our method compared to existing radiance-field-based SLAM systems.
  project_page: https://xingxingzuo.github.io/gaussian_lic/
  paper: https://arxiv.org/pdf/2404.06926
  code: null
  video: null
  tags:
  - Project
  - SLAM
  thumbnail: assets/thumbnails/lang2024gaussianlic.jpg
  publication_date: '2024-04-10T11:24:34+00:00'
- id: zhou2024dreamscene360
  title: 'DreamScene360: Unconstrained Text-to-3D Scene Generation with Panoramic
    Gaussian Splatting'
  authors: Shijie Zhou, Zhiwen Fan, Dejia Xu, Haoran Chang, Pradyumna Chari, Tejas
    Bharadwaj, Suya You, Zhangyang Wang, Achuta Kadambi
  year: '2024'
  abstract: The increasing demand for virtual reality applications has highlighted
    the significance of crafting immersive 3D assets. We present a text-to-3D 360∘
    scene generation pipeline that facilitates the creation of comprehensive 360∘
    scenes for in-the-wild environments in a matter of minutes. Our approach utilizes
    the generative power of a 2D diffusion model and prompt self-refinement to create
    a high-quality and globally coherent panoramic image. This image acts as a preliminary
    "flat" (2D) scene representation. Subsequently, it is lifted into 3D Gaussians,
    employing splatting techniques to enable real-time exploration. To produce consistent
    3D geometry, our pipeline constructs a spatially coherent structure by aligning
    the 2D monocular depth into a globally optimized point cloud. This point cloud
    serves as the initial state for the centroids of 3D Gaussians. In order to address
    invisible issues inherent in single-view inputs, we impose semantic and geometric
    constraints on both synthesized and input camera views as regularizations. These
    guide the optimization of Gaussians, aiding in the reconstruction of unseen regions.
    In summary, our method offers a globally consistent 3D scene within a 360∘ perspective,
    providing an enhanced immersive experience over existing techniques.
  project_page: https://dreamscene360.github.io/
  paper: https://arxiv.org/pdf/2404.06903
  code: https://github.com/ShijieZhou-UCLA/DreamScene360
  video: https://www.youtube.com/embed/6rMIQfe7b24?si=cm7cZ-T9r5na7YFD
  tags:
  - Code
  - Diffusion
  - Project
  - Video
  thumbnail: assets/thumbnails/zhou2024dreamscene360.jpg
  publication_date: '2024-04-10T10:46:59+00:00'
- id: kruse2024splatpose
  title: 'SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection'
  authors: Mathis Kruse, Marco Rudolph, Dominik Woiwode, Bodo Rosenhahn
  year: '2024'
  abstract: Detecting anomalies in images has become a well-explored problem in both
    academia and industry. State-of-the-art algorithms are able to detect defects
    in increasingly difficult settings and data modalities. However, most current
    methods are not suited to address 3D objects captured from differing poses. While
    solutions using Neural Radiance Fields (NeRFs) have been proposed, they suffer
    from excessive computation requirements, which hinder real-world usability. For
    this reason, we propose the novel 3D Gaussian splatting-based framework SplatPose
    which, given multi-view images of a 3D object, accurately estimates the pose of
    unseen views in a differentiable manner, and detects anomalies in them. We achieve
    state-of-the-art results in both training and inference speed, and detection performance,
    even when using less training data than competing methods. We thoroughly evaluate
    our framework using the recently proposed Pose-agnostic Anomaly Detection benchmark
    and its multi-pose anomaly detection (MAD) data set.
  project_page: null
  paper: https://arxiv.org/pdf/2404.06832
  code: https://github.com/m-kruse98/SplatPose
  video: null
  tags:
  - Code
  - Misc
  - Poses
  thumbnail: assets/thumbnails/kruse2024splatpose.jpg
  publication_date: '2024-04-10T08:48:09+00:00'
- id: huang2024zeroshot
  title: Zero-shot Point Cloud Completion Via 2D Priors
  authors: Tianxin Huang, Zhiwen Yan, Yuyang Zhao, Gim Hee Lee
  year: '2024'
  abstract: 3D point cloud completion is designed to recover complete shapes from
    partially observed point clouds. Conventional completion methods typically depend
    on extensive point cloud data for training %, with their effectiveness often constrained
    to object categories similar to those seen during training. In contrast, we propose
    a zero-shot framework aimed at completing partially observed point clouds across
    any unseen categories. Leveraging point rendering via Gaussian Splatting, we develop
    techniques of Point Cloud Colorization and Zero-shot Fractal Completion that utilize
    2D priors from pre-trained diffusion models to infer missing regions. Experimental
    results on both synthetic and real-world scanned point clouds demonstrate that
    our approach outperforms existing methods in completing a variety of objects without
    any requirement for specific training data.
  project_page: null
  paper: https://arxiv.org/pdf/2404.06814
  code: null
  video: null
  tags:
  - Diffusion
  - Point Cloud
  thumbnail: assets/thumbnails/huang2024zeroshot.jpg
  publication_date: '2024-04-10T08:02:17+00:00'
- id: dai2024spikenvs
  title: 'SpikeNVS: Enhancing Novel View Synthesis from Blurry Images via Spike Camera'
  authors: Gaole Dai, Zhenyu Wang, Qinwen Xu, Ming Lu, Wen Chen, Boxin Shi, Shanghang
    Zhang, Tiejun Huang
  year: '2024'
  abstract: One of the most critical factors in achieving sharp Novel View Synthesis
    (NVS) using neural field methods like Neural Radiance Fields (NeRF) and 3D Gaussian
    Splatting (3DGS) is the quality of the training images. However, Conventional
    RGB cameras are susceptible to motion blur. In contrast, neuromorphic cameras
    like event and spike cameras inherently capture more comprehensive temporal information,
    which can provide a sharp representation of the scene as additional training data.
    Recent methods have explored the integration of event cameras to improve the quality
    of NVS. The event-RGB approaches have some limitations, such as high training
    costs and the inability to work effectively in the background. Instead, our study
    introduces a new method that uses the spike camera to overcome these limitations.
    By considering texture reconstruction from spike streams as ground truth, we design
    the Texture from Spike (TfS) loss. Since the spike camera relies on temporal integration
    instead of temporal differentiation used by event cameras, our proposed TfS loss
    maintains manageable training costs. It handles foreground objects with backgrounds
    simultaneously. We also provide a real-world dataset captured with our spike-RGB
    camera system to facilitate future research endeavors. We conduct extensive experiments
    using synthetic and real-world datasets to demonstrate that our design can enhance
    novel view synthesis across NeRF and 3DGS.
  project_page: null
  paper: https://arxiv.org/pdf/2404.06710
  code: null
  video: null
  tags:
  - Deblurring
  - Misc
  thumbnail: assets/thumbnails/dai2024spikenvs.jpg
  publication_date: '2024-04-10T03:31:32+00:00'
- id: wang2024endtoend
  title: End-to-End Rate-Distortion Optimized 3D Gaussian Representation
  authors: Henan Wang, Hanxin Zhu, Tianyu He, Runsen Feng, Jiajun Deng, Jiang Bian,
    Zhibo Chen
  year: '2024'
  abstract: '3D Gaussian Splatting (3DGS) has become an emerging technique with remarkable
    potential in 3D representation and image rendering. However, the substantial storage
    overhead of 3DGS significantly impedes its practical applications. In this work,
    we formulate the compact 3D Gaussian learning as an end-to-end Rate-Distortion
    Optimization (RDO) problem and propose RDO-Gaussian that can achieve flexible
    and continuous rate control. RDO-Gaussian addresses two main issues that exist
    in current schemes: 1) Different from prior endeavors that minimize the rate under
    the fixed distortion, we introduce dynamic pruning and entropy-constrained vector
    quantization (ECVQ) that optimize the rate and distortion at the same time. 2)
    Previous works treat the colors of each Gaussian equally, while we model the colors
    of different regions and materials with learnable numbers of parameters. We verify
    our method on both real and synthetic scenes, showcasing that RDO-Gaussian greatly
    reduces the size of 3D Gaussian over 40×, and surpasses existing methods in rate-distortion
    performance.'
  project_page: https://rdogaussian.github.io/
  paper: https://arxiv.org/pdf/2406.01597.pdf
  code: https://github.com/USTC-IMCL/RDO-Gaussian
  video: null
  tags:
  - Code
  - Compression
  - Project
  thumbnail: assets/thumbnails/wang2024endtoend.jpg
  publication_date: '2024-04-09T14:37:54+00:00'
- id: lu20243d
  title: 3D Geometry-aware Deformable Gaussian Splatting for Dynamic View Synthesis
  authors: Zhicheng Lu, Xiang Guo, Le Hui, Tianrui Chen, Min Yang, Xiao Tang, Feng
    Zhu, Yuchao Dai
  year: '2024'
  abstract: In this paper, we propose a 3D geometry-aware deformable Gaussian Splatting
    method for dynamic view synthesis. Existing neural radiance fields (NeRF) based
    solutions learn the deformation in an implicit manner, which cannot incorporate
    3D scene geometry. Therefore, the learned deformation is not necessarily geometrically
    coherent, which results in unsatisfactory dynamic view synthesis and 3D dynamic
    reconstruction. Recently, 3D Gaussian Splatting provides a new representation
    of the 3D scene, building upon which the 3D geometry could be exploited in learning
    the complex 3D deformation. Specifically, the scenes are represented as a collection
    of 3D Gaussian, where each 3D Gaussian is optimized to move and rotate over time
    to model the deformation. To enforce the 3D scene geometry constraint during deformation,
    we explicitly extract 3D geometry features and integrate them in learning the
    3D deformation. In this way, our solution achieves 3D geometry-aware deformation
    modeling, which enables improved dynamic view synthesis and 3D dynamic reconstruction.
    Extensive experimental results on both synthetic and real datasets prove the superiority
    of our solution, which achieves new state-of-the-art performance.
  project_page: https://npucvr.github.io/GaGS/
  paper: https://arxiv.org/pdf/2404.06270
  code: https://github.com/zhichengLuxx/GaGS
  video: null
  tags:
  - Code
  - Dynamic
  - Project
  thumbnail: assets/thumbnails/lu20243d.jpg
  publication_date: '2024-04-09T12:47:30+00:00'
- id: bonilla2024gaussian
  title: 'Gaussian Pancakes: Geometrically-Regularized 3D Gaussian Splatting for Realistic
    Endoscopic Reconstruction'
  authors: Sierra Bonilla, Shuai Zhang, Dimitrios Psychogyios, Danail Stoyanov, Francisco
    Vasconcelos, Sophia Bano
  year: '2024'
  abstract: Within colorectal cancer diagnostics, conventional colonoscopy techniques
    face critical limitations, including a limited field of view and a lack of depth
    information, which can impede the detection of precancerous lesions. Current methods
    struggle to provide comprehensive and accurate 3D reconstructions of the colonic
    surface which can help minimize the missing regions and reinspection for pre-cancerous
    polyps. Addressing this, we introduce 'Gaussian Pancakes', a method that leverages
    3D Gaussian Splatting (3D GS) combined with a Recurrent Neural Network-based Simultaneous
    Localization and Mapping (RNNSLAM) system. By introducing geometric and depth
    regularization into the 3D GS framework, our approach ensures more accurate alignment
    of Gaussians with the colon surface, resulting in smoother 3D reconstructions
    with novel viewing of detailed textures and structures. Evaluations across three
    diverse datasets show that Gaussian Pancakes enhances novel view synthesis quality,
    surpassing current leading methods with a 18% boost in PSNR and a 16% improvement
    in SSIM. It also delivers over 100X faster rendering and more than 10X shorter
    training times, making it a practical tool for real-time applications. Hence,
    this holds promise for achieving clinical translation for better detection and
    diagnosis of colorectal cancer.
  project_page: https://papers.miccai.org/miccai-2024/349-Paper2298.html
  paper: https://arxiv.org/pdf/2404.06128
  code: https://github.com/smbonilla/GaussianPancakes
  video: null
  tags:
  - Code
  - Medicine
  - Project
  thumbnail: assets/thumbnails/bonilla2024gaussian.jpg
  publication_date: '2024-04-09T08:51:44+00:00'
- id: rota2024revising
  title: Revising Densification in Gaussian Splatting
  authors: Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder
  year: '2024'
  abstract: In this paper, we address the limitations of Adaptive Density Control
    (ADC) in 3D Gaussian Splatting (3DGS), a scene representation method achieving
    high-quality, photorealistic results for novel view synthesis. ADC has been introduced
    for automatic 3D point primitive management, controlling densification and pruning,
    however, with certain limitations in the densification logic. Our main contribution
    is a more principled, pixel-error driven formulation for density control in 3DGS,
    leveraging an auxiliary, per-pixel error function as the criterion for densification.
    We further introduce a mechanism to control the total number of primitives generated
    per scene and correct a bias in the current opacity handling strategy of ADC during
    cloning operations. Our approach leads to consistent quality improvements across
    a variety of benchmark scenes, without sacrificing the method's efficiency.
  project_page: null
  paper: https://arxiv.org/pdf/2404.06109
  code: null
  video: null
  tags:
  - Densification
  thumbnail: assets/thumbnails/rota2024revising.jpg
  publication_date: '2024-04-09T08:20:37+00:00'
- id: yang2024hash3d
  title: 'Hash3D: Training-free Acceleration for 3D Generation'
  authors: Xingyi Yang, Xinchao Wang
  year: '2024'
  abstract: The evolution of 3D generative modeling has been notably propelled by
    the adoption of 2D diffusion models. Despite this progress, the cumbersome optimization
    process per se presents a critical hurdle to efficiency. In this paper, we introduce
    Hash3D, a universal acceleration for 3D generation without model training. Central
    to Hash3D is the insight that feature-map redundancy is prevalent in images rendered
    from camera positions and diffusion time-steps in close proximity. By effectively
    hashing and reusing these feature maps across neighboring timesteps and camera
    angles, Hash3D substantially prevents redundant calculations, thus accelerating
    the diffusion model's inference in 3D generation tasks. We achieve this through
    an adaptive grid-based hashing. Surprisingly, this feature-sharing mechanism not
    only speed up the generation but also enhances the smoothness and view consistency
    of the synthesized 3D objects. Our experiments covering 5 text-to-3D and 3 image-to-3D
    models, demonstrate Hash3D's versatility to speed up optimization, enhancing efficiency
    by 1.3 to 4 times. Additionally, Hash3D's integration with 3D Gaussian splatting
    largely speeds up 3D model creation, reducing text-to-3D processing to about 10
    minutes and image-to-3D conversion to roughly 30 seconds.
  project_page: https://adamdad.github.io/hash3D/
  paper: https://arxiv.org/pdf/2404.06091
  code: https://github.com/Adamdad/hash3D
  video: null
  tags:
  - Acceleration
  - Code
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/yang2024hash3d.jpg
  publication_date: '2024-04-09T07:49:30+00:00'
- id: zhang2024stylizedgs
  title: 'StylizedGS: Controllable Stylization for 3D Gaussian Splatting'
  authors: Dingxi Zhang, Zhuoxun Chen, Yu-Jie Yuan, Fang-Lue Zhang, Zhenliang He,
    Shiguang Shan, Lin Gao
  year: '2024'
  abstract: With the rapid development of XR, 3D generation and editing are becoming
    more and more important, among which, stylization is an important tool of 3D appearance
    editing. It can achieve consistent 3D artistic stylization given a single reference
    style image and thus is a user-friendly editing way. However, recent NeRF-based
    3D stylization methods face efficiency issues that affect the actual user experience
    and the implicit nature limits its ability to transfer the geometric pattern styles.
    Additionally, the ability for artists to exert flexible control over stylized
    scenes is considered highly desirable, fostering an environment conducive to creative
    exploration. In this paper, we introduce StylizedGS, a 3D neural style transfer
    framework with adaptable control over perceptual factors based on 3D Gaussian
    Splatting (3DGS) representation. The 3DGS brings the benefits of high efficiency.
    We propose a GS filter to eliminate floaters in the reconstruction which affects
    the stylization effects before stylization. Then the nearest neighbor-based style
    loss is introduced to achieve stylization by fine-tuning the geometry and color
    parameters of 3DGS, while a depth preservation loss with other regularizations
    is proposed to prevent the tampering of geometry content. Moreover, facilitated
    by specially designed losses, StylizedGS enables users to control color, stylized
    scale and regions during the stylization to possess customized capabilities. Our
    method can attain high-quality stylization results characterized by faithful brushstrokes
    and geometric consistency with flexible controls. Extensive experiments across
    various scenes and styles demonstrate the effectiveness and efficiency of our
    method concerning both stylization quality and inference FPS.
  project_page: null
  paper: https://arxiv.org/pdf/2404.05220
  code: null
  video: null
  tags:
  - Rendering
  - Style Transfer
  thumbnail: assets/thumbnails/zhang2024stylizedgs.jpg
  publication_date: '2024-04-08T06:32:11+00:00'
- id: wu2024dualcamera
  title: Dual-Camera Smooth Zoom on Mobile Phones
  authors: Renlong Wu, Zhilu Zhang, Yu Yang, Wangmeng Zuo
  year: '2024'
  abstract: When zooming between dual cameras on a mobile, noticeable jumps in geometric
    content and image color occur in the preview, inevitably affecting the user's
    zoom experience. In this work, we introduce a new task, ie, dual-camera smooth
    zoom (DCSZ) to achieve a smooth zoom preview. The frame interpolation (FI) technique
    is a potential solution but struggles with ground-truth collection. To address
    the issue, we suggest a data factory solution where continuous virtual cameras
    are assembled to generate DCSZ data by rendering reconstructed 3D models of the
    scene. In particular, we propose a novel dual-camera smooth zoom Gaussian Splatting
    (ZoomGS), where a camera-specific encoding is introduced to construct a specific
    3D model for each virtual camera. With the proposed data factory, we construct
    a synthetic dataset for DCSZ, and we utilize it to fine-tune FI models. In addition,
    we collect real-world dual-zoom images without ground-truth for evaluation. Extensive
    experiments are conducted with multiple FI methods. The results show that the
    fine-tuned FI models achieve a significant performance improvement over the original
    ones on DCSZ task.
  project_page: https://dualcamerasmoothzoom.github.io/
  paper: https://arxiv.org/pdf/2404.04908
  code: https://github.com/ZcsrenlongZ/ZoomGS
  video: null
  tags:
  - Code
  - Misc
  - Project
  - Rendering
  thumbnail: assets/thumbnails/wu2024dualcamera.jpg
  publication_date: '2024-04-07T10:28:01+00:00'
- id: darmon2024robust
  title: Robust Gaussian Splatting
  authors: François Darmon, Lorenzo Porzi, Samuel Rota-Bulò, Peter Kontschieder
  year: '2024'
  abstract: In this paper, we address common error sources for 3D Gaussian Splatting
    (3DGS) including blur, imperfect camera poses, and color inconsistencies, with
    the goal of improving its robustness for practical applications like reconstructions
    from handheld phone captures. Our main contribution involves modeling motion blur
    as a Gaussian distribution over camera poses, allowing us to address both camera
    pose refinement and motion blur correction in a unified way. Additionally, we
    propose mechanisms for defocus blur compensation and for addressing color in-consistencies
    caused by ambient light, shadows, or due to camera-related factors like varying
    white balancing settings. Our proposed solutions integrate in a seamless way with
    the 3DGS formulation while maintaining its benefits in terms of training efficiency
    and rendering speed. We experimentally validate our contributions on relevant
    benchmark datasets including Scannet++ and Deblur-NeRF, obtaining state-of-the-art
    results and thus consistent improvements over relevant baselines.
  project_page: null
  paper: https://arxiv.org/pdf/2404.04211
  code: null
  video: null
  tags:
  - Deblurring
  - Rendering
  thumbnail: assets/thumbnails/darmon2024robust.jpg
  publication_date: '2024-04-05T16:42:16+00:00'
- id: wu2024mmgaussian
  title: 'MM-Gaussian: 3D Gaussian-based Multi-modal Fusion for Localization and Reconstruction
    in Unbounded Scenes'
  authors: Chenyang Wu, Yifan Duan, Xinran Zhang, Yu Sheng, Jianmin Ji, Yanyong Zhang
  year: '2024'
  abstract: Localization and mapping are critical tasks for various applications such
    as autonomous vehicles and robotics. The challenges posed by outdoor environments
    present particular complexities due to their unbounded characteristics. In this
    work, we present MM-Gaussian, a LiDAR-camera multi-modal fusion system for localization
    and mapping in unbounded scenes. Our approach is inspired by the recently developed
    3D Gaussians, which demonstrate remarkable capabilities in achieving high rendering
    quality and fast rendering speed. Specifically, our system fully utilizes the
    geometric structure information provided by solid-state LiDAR to address the problem
    of inaccurate depth encountered when relying solely on visual solutions in unbounded,
    outdoor scenarios. Additionally, we utilize 3D Gaussian point clouds, with the
    assistance of pixel-level gradient descent, to fully exploit the color information
    in photos, thereby achieving realistic rendering effects. To further bolster the
    robustness of our system, we designed a relocalization module, which assists in
    returning to the correct trajectory in the event of a localization failure. Experiments
    conducted in multiple scenarios demonstrate the effectiveness of our method.
  project_page: null
  paper: https://arxiv.org/pdf/2404.04026
  code: null
  video: null
  tags:
  - Poses
  thumbnail: assets/thumbnails/wu2024mmgaussian.jpg
  publication_date: '2024-04-05T11:14:19+00:00'
- id: wu2024sc4d
  title: 'SC4D: Sparse-Controlled Video-to-4D Generation and Motion Transfer'
  authors: Zijie Wu, Chaohui Yu, Yanqin Jiang, Chenjie Cao, Fan Wang, Xiang Bai
  year: '2024'
  abstract: Recent advances in 2D/3D generative models enable the generation of dynamic
    3D objects from a single-view video. Existing approaches utilize score distillation
    sampling to form the dynamic scene as dynamic NeRF or dense 3D Gaussians. However,
    these methods struggle to strike a balance among reference view alignment, spatio-temporal
    consistency, and motion fidelity under single-view conditions due to the implicit
    nature of NeRF or the intricate dense Gaussian motion prediction. To address these
    issues, this paper proposes an efficient, sparse-controlled video-to-4D framework
    named SC4D, that decouples motion and appearance to achieve superior video-to-4D
    generation. Moreover, we introduce Adaptive Gaussian (AG) initialization and Gaussian
    Alignment (GA) loss to mitigate shape degeneration issue, ensuring the fidelity
    of the learned motion and shape. Comprehensive experimental results demonstrate
    that our method surpasses existing methods in both quality and efficiency. In
    addition, facilitated by the disentangled modeling of motion and appearance of
    SC4D, we devise a novel application that seamlessly transfers the learned motion
    onto a diverse array of 4D entities according to textual descriptions.
  project_page: https://sc4d.github.io/
  paper: https://arxiv.org/pdf/2404.03736
  code: https://github.com/JarrentWu1031/SC4D
  video: https://youtu.be/SkpTEuX4B5c?si=yvrF_iRHnMQR9TD0
  tags:
  - Code
  - Diffusion
  - Project
  - Video
  thumbnail: assets/thumbnails/wu2024sc4d.jpg
  publication_date: '2024-04-04T18:05:18+00:00'
- id: bae2024pergaussian
  title: Per-Gaussian Embedding-Based Deformation for Deformable 3D Gaussian Splatting
  authors: Jeongmin Bae*, Seoha Kim*, Youngsik Yun, Hahyun Lee, Gun Bang, Youngjung
    Uh
  year: '2024'
  abstract: As 3D Gaussian Splatting (3DGS) provides fast and high-quality novel view
    synthesis, it is a natural extension to deform a canonical 3DGS to multiple frames
    for representing a dynamic scene. However, previous works fail to accurately reconstruct
    complex dynamic scenes. We attribute the failure to the design of the deformation
    field, which is built as a coordinate-based function. This approach is problematic
    because 3DGS is a mixture of multiple fields centered at the Gaussians, not just
    a single coordinate-based framework. To resolve this problem, we define the deformation
    as a function of per-Gaussian embeddings and temporal embeddings. Moreover, we
    decompose deformations as coarse and fine deformations to model slow and fast
    movements, respectively. Also, we introduce a local smoothness regularization
    for per-Gaussian embedding to improve the details in dynamic regions.
  project_page: https://jeongminb.github.io/e-d3dgs/
  paper: https://arxiv.org/pdf/2404.03613
  code: https://github.com/JeongminB/E-D3DGS
  video: null
  tags:
  - Code
  - Dynamic
  - Project
  thumbnail: assets/thumbnails/bae2024pergaussian.jpg
  publication_date: '2024-04-04T17:34:41+00:00'
- id: li2024dreamscene
  title: 'DreamScene: 3D Gaussian-based Text-to-3D Scene Generation via Formation
    Pattern Sampling'
  authors: Haoran Li, Haolin Shi, Wenli Zhang, Wenjun Wu, Yong Liao, Lin Wang, Lik
    Hang Lee, Pengyuan Zhou
  year: '2024'
  abstract: Text-to-3D scene generation holds immense potential for the gaming, film,
    and architecture sectors, increasingly capturing the attention of both academic
    and industry circles. Despite significant progress, current methods still struggle
    with maintaining high quality, consistency, and editing flexibility. In this paper,
    we propose DreamScene, a 3D Gaussian-based novel text-to-3D scene generation framework
    that leverages Formation Pattern Sampling (FPS) for core structuring, augmented
    with a strategic camera sampling and supported by holistic object-environment
    integration to overcome these hurdles. FPS, guided by the formation patterns of
    3D objects, employs multi-timesteps sampling to quickly form semantically rich,
    high-quality representations, uses 3D Gaussian filtering for optimization stability,
    and leverages reconstruction techniques to generate plausible textures. The camera
    sampling strategy incorporates a progressive three-stage approach, specifically
    designed for both indoor and outdoor settings, to effectively ensure scene-wide
    3D consistency. DreamScene enhances scene editing flexibility by combining objects
    and environments, enabling targeted adjustments. Extensive experiments showcase
    DreamScene's superiority over current state-of-the-art techniques, heralding its
    wide-ranging potential for diverse applications.
  project_page: https://dreamscene-project.github.io/
  paper: https://arxiv.org/pdf/2404.03575.pdf
  code: https://github.com/DreamScene-Project/DreamScene
  video: null
  tags:
  - Code
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/li2024dreamscene.jpg
  publication_date: '2024-04-04T16:38:57+00:00'
- id: meng2024omnigs
  title: 'OmniGS: Omnidirectional Gaussian Splatting for Fast Radiance Field Reconstruction
    using Omnidirectional Images'
  authors: Jiarui Meng, Haijie Li, Yanmin Wu, Qiankun Gao, Shuzhou Yang, Jian Zhang,
    Siwei Ma
  year: '2024'
  abstract: Photorealistic reconstruction relying on 3D Gaussian Splatting has shown
    promising potential in robotics. However, the current 3D Gaussian Splatting system
    only supports radiance field reconstruction using undistorted perspective images.
    In this paper, we present OmniGS, a novel omnidirectional Gaussian splatting system,
    to take advantage of omnidirectional images for fast radiance field reconstruction.
    Specifically, we conduct a theoretical analysis of spherical camera model derivatives
    in 3D Gaussian Splatting. According to the derivatives, we then implement a new
    GPU-accelerated omnidirectional rasterizer that directly splats 3D Gaussians onto
    the equirectangular screen space for omnidirectional image rendering. As a result,
    we realize differentiable optimization of the radiance field without the requirement
    of cube-map rectification or tangent-plane approximation. Extensive experiments
    conducted in egocentric and roaming scenarios demonstrate that our method achieves
    state-of-the-art reconstruction quality and high rendering speed using omnidirectional
    images. To benefit the research community, the code will be made publicly available
    once the paper is published.
  project_page: https://liquorleaf.github.io/research/OmniGS/
  paper: https://arxiv.org/pdf/2404.03202
  code: null
  video: null
  tags:
  - 360 degree
  - Project
  - Rendering
  thumbnail: assets/thumbnails/meng2024omnigs.jpg
  publication_date: '2024-04-04T05:10:26+00:00'
- id: nikolakakis2024gaspct
  title: 'GaSpCT: Gaussian Splatting for Novel CT Projection View Synthesis'
  authors: Emmanouil Nikolakakis, Utkarsh Gupta, Jonathan Vengosh, Justin Bui, Razvan
    Marinescu
  year: '2024'
  abstract: 'We present GaSpCT, a novel view synthesis and 3D scene representation
    method used to generate novel projection views for Computer Tomography (CT) scans.
    We adapt the Gaussian Splatting framework to enable novel view synthesis in CT
    based on limited sets of 2D image projections and without the need for Structure
    from Motion (SfM) methodologies. Therefore, we reduce the total scanning duration
    and the amount of radiation dose the patient receives during the scan. We adapted
    the loss function to our use-case by encouraging a stronger background and foreground
    distinction using two sparsity promoting regularizers: a beta loss and a total
    variation (TV) loss. Finally, we initialize the Gaussian locations across the
    3D space using a uniform prior distribution of where the brain''s positioning
    would be expected to be within the field of view. We evaluate the performance
    of our model using brain CT scans from the Parkinson''s Progression Markers Initiative
    (PPMI) dataset and demonstrate that the rendered novel views closely match the
    original projection views of the simulated scan, and have better performance than
    other implicit 3D scene representations methodologies. Furthermore, we empirically
    observe reduced training time compared to neural network based image synthesis
    for sparse-view CT image reconstruction. Finally, the memory requirements of the
    Gaussian Splatting representations are reduced by 17% compared to the equivalent
    voxel grid image representations.'
  project_page: null
  paper: https://arxiv.org/pdf/2404.03126
  code: null
  video: null
  tags:
  - Medicine
  thumbnail: assets/thumbnails/nikolakakis2024gaspct.jpg
  publication_date: '2024-04-04T00:28:50+00:00'
- id: zhao2024tclcgs
  title: 'TCLC-GS: Tightly Coupled LiDAR-Camera Gaussian Splatting for Surrounding
    Autonomous Driving Scenes'
  authors: Cheng Zhao, Su Sun, Ruoyu Wang, Yuliang Guo, Jun-Jun Wan, Zhou Huang, Xinyu
    Huang, Yingjie Victor Chen, Liu Ren
  year: '2024'
  abstract: Most 3D Gaussian Splatting (3D-GS) based methods for urban scenes initialize
    3D Gaussians directly with 3D LiDAR points, which not only underutilizes LiDAR
    data capabilities but also overlooks the potential advantages of fusing LiDAR
    with camera data. In this paper, we design a novel tightly coupled LiDAR-Camera
    Gaussian Splatting (TCLC-GS) to fully leverage the combined strengths of both
    LiDAR and camera sensors, enabling rapid, high-quality 3D reconstruction and novel
    view RGB/depth synthesis. TCLC-GS designs a hybrid explicit (colorized 3D mesh)
    and implicit (hierarchical octree feature) 3D representation derived from LiDAR-camera
    data, to enrich the properties of 3D Gaussians for splatting. 3D Gaussian's properties
    are not only initialized in alignment with the 3D mesh which provides more completed
    3D shape and color information, but are also endowed with broader contextual information
    through retrieved octree implicit features. During the Gaussian Splatting optimization
    process, the 3D mesh offers dense depth information as supervision, which enhances
    the training process by learning of a robust geometry. Comprehensive evaluations
    conducted on the Waymo Open Dataset and nuScenes Dataset validate our method's
    state-of-the-art (SOTA) performance. Utilizing a single NVIDIA RTX 3090 Ti, our
    method demonstrates fast training and achieves real-time RGB and depth rendering
    at 90 FPS in resolution of 1920x1280 (Waymo), and 120 FPS in resolution of 1600x900
    (nuScenes) in urban scenarios.
  project_page: null
  paper: https://arxiv.org/pdf/2404.02410.pdf
  code: null
  video: https://www.youtube.com/watch?v=CEo6mZ6FGg0
  tags:
  - Autonomous Driving
  - Lidar
  - Video
  thumbnail: assets/thumbnails/zhao2024tclcgs.jpg
  publication_date: '2024-04-03T02:26:15+00:00'
- id: wolf2024gs2mesh
  title: 'GS2Mesh: Surface Reconstruction from Gaussian Splatting via Novel Stereo
    Views'
  authors: Yaniv Wolf, Amit Bracha, Ron Kimmel
  year: '2024'
  abstract: Recently, 3D Gaussian Splatting (3DGS) has emerged as an efficient approach
    for accurately representing scenes. However, despite its superior novel view synthesis
    capabilities, extracting the geometry of the scene directly from the Gaussian
    properties remains a challenge, as those are optimized based on a photometric
    loss. While some concurrent models have tried adding geometric constraints during
    the Gaussian optimization process, they still produce noisy, unrealistic surfaces.
    We propose a novel approach for bridging the gap between the noisy 3DGS representation
    and the smooth 3D mesh representation, by injecting real-world knowledge into
    the depth extraction process. Instead of extracting the geometry of the scene
    directly from the Gaussian properties, we instead extract the geometry through
    a pre-trained stereo-matching model. We render stereo-aligned pairs of images
    corresponding to the original training poses, feed the pairs into a stereo model
    to get a depth profile, and finally fuse all of the profiles together to get a
    single mesh. The resulting reconstruction is smoother, more accurate and shows
    more intricate details compared to other methods for surface reconstruction from
    Gaussian Splatting, while only requiring a small overhead on top of the fairly
    short 3DGS optimization process. We performed extensive testing of the proposed
    method on in-the-wild scenes, obtained using a smartphone, showcasing its superior
    reconstruction abilities. Additionally, we tested the method on the Tanks and
    Temples and DTU benchmarks, achieving state-of-the-art results.
  project_page: https://gs2mesh.github.io//
  paper: https://arxiv.org/pdf/2404.01810
  code: https://github.com/yanivw12/gs2mesh
  video: https://youtu.be/cjtmLDD8YZk
  tags:
  - 2DGS
  - Code
  - Meshing
  - Project
  - Stereo
  - Video
  thumbnail: assets/thumbnails/wolf2024gs2mesh.jpg
  publication_date: '2024-04-02T10:13:18+00:00'
- id: qiu2024feature
  title: 'Feature Splatting: Language-Driven Physics-Based Scene Synthesis and Editing'
  authors: Ri-Zhao Qiu, Ge Yang, Weijia Zeng, Xiaolong Wang
  year: '2024'
  abstract: Scene representations using 3D Gaussian primitives have produced excellent
    results in modeling the appearance of static and dynamic 3D scenes. Many graphics
    applications, however, demand the ability to manipulate both the appearance and
    the physical properties of objects. We introduce Feature Splatting, an approach
    that unifies physics-based dynamic scene synthesis with rich semantics from vision
    language foundation models that are grounded by natural language. Our first contribution
    is a way to distill high-quality, object-centric vision-language features into
    3D Gaussians, that enables semi-automatic scene decomposition using text queries.
    Our second contribution is a way to synthesize physics-based dynamics from an
    otherwise static scene using a particle-based simulator, in which material properties
    are assigned automatically via text queries. We ablate key techniques used in
    this pipeline, to illustrate the challenge and opportunities in using feature-carrying
    3D Gaussians as a unified format for appearance, geometry, material properties
    and semantics grounded on natural language.
  project_page: https://feature-splatting.github.io/
  paper: https://arxiv.org/pdf/2404.01223
  code: https://github.com/vuer-ai/feature_splatting
  video: https://feature-splatting.github.io/resources/teaser_overview.mp4
  tags:
  - Code
  - Editing
  - Language Embedding
  - Physics
  - Project
  - Segmentation
  - Video
  thumbnail: assets/thumbnails/qiu2024feature.jpg
  publication_date: '2024-04-01T16:31:04+00:00'
- id: meng2024mirror3dgs
  title: 'Mirror-3DGS: Incorporating Mirror Reflections into 3D Gaussian Splatting'
  authors: Jiarui Meng, Haijie Li, Yanmin Wu, Qiankun Gao, Shuzhou Yang, Jian Zhang,
    Siwei Ma
  year: '2024'
  abstract: 3D Gaussian Splatting (3DGS) has marked a significant breakthrough in
    the realm of 3D scene reconstruction and novel view synthesis. However, 3DGS,
    much like its predecessor Neural Radiance Fields (NeRF), struggles to accurately
    model physical reflections, particularly in mirrors that are ubiquitous in real-world
    scenes. This oversight mistakenly perceives reflections as separate entities that
    physically exist, resulting in inaccurate reconstructions and inconsistent reflective
    properties across varied viewpoints. To address this pivotal challenge, we introduce
    Mirror-3DGS, an innovative rendering framework devised to master the intricacies
    of mirror geometries and reflections, paving the way for the generation of realistically
    depicted mirror reflections. By ingeniously incorporating mirror attributes into
    the 3DGS and leveraging the principle of plane mirror imaging, Mirror-3DGS crafts
    a mirrored viewpoint to observe from behind the mirror, enriching the realism
    of scene renderings. Extensive assessments, spanning both synthetic and real-world
    scenes, showcase our method's ability to render novel views with enhanced fidelity
    in real-time, surpassing the state-of-the-art Mirror-NeRF specifically within
    the challenging mirror regions.
  project_page: https://mirror-gaussian.github.io/
  paper: https://arxiv.org/pdf/2404.01168.pdf
  code: null
  video: null
  tags:
  - Project
  - Rendering
  thumbnail: assets/thumbnails/meng2024mirror3dgs.jpg
  publication_date: '2024-04-01T15:16:33+00:00'
- id: liu2024citygaussian
  title: 'CityGaussian: Real-time High-quality Large-Scale Scene Rendering with Gaussians'
  authors: Yang Liu, He Guan, Chuanchen Luo, Lue Fan, Naiyan Wang, Junran Peng, Zhaoxiang
    Zhang
  year: '2024'
  abstract: The advancement of real-time 3D scene reconstruction and novel view synthesis
    has been significantly propelled by 3D Gaussian Splatting (3DGS). However, effectively
    training large-scale 3DGS and rendering it in real-time across various scales
    remains challenging. This paper introduces CityGaussian (CityGS), which employs
    a novel divide-and-conquer training approach and Level-of-Detail (LoD) strategy
    for efficient large-scale 3DGS training and rendering. Specifically, the global
    scene prior and adaptive training data selection enables efficient training and
    seamless fusion. Based on fused Gaussian primitives, we generate different detail
    levels through compression, and realize fast rendering across various scales through
    the proposed block-wise detail levels selection and aggregation strategy. Extensive
    experimental results on large-scale scenes demonstrate that our approach attains
    state-of-theart rendering quality, enabling consistent real-time rendering of
    largescale scenes across vastly different scales.
  project_page: https://dekuliutesla.github.io/citygs/
  paper: https://arxiv.org/pdf/2404.01133
  code: https://github.com/DekuLiuTesla/CityGaussian
  video: null
  tags:
  - Code
  - Large-Scale
  - Project
  thumbnail: assets/thumbnails/liu2024citygaussian.jpg
  publication_date: '2024-04-01T14:24:40+00:00'
- id: shao2024haha
  title: 'HAHA: Highly Articulated Gaussian Human Avatars with Textured Mesh Prior'
  authors: Zhijing Shao, Zhaolong Wang, Zhuang Li, Duotun Wang, Xiangru Lin, Yu Zhang,
    Mingming Fan, Zeyu Wang
  year: '2024'
  abstract: 'We present HAHA - a novel approach for animatable human avatar generation
    from monocular input videos. The proposed method relies on learning the trade-off
    between the use of Gaussian splatting and a textured mesh for efficient and high
    fidelity rendering. We demonstrate its efficiency to animate and render full-body
    human avatars controlled via the SMPL-X parametric model. Our model learns to
    apply Gaussian splatting only in areas of the SMPL-X mesh where it is necessary,
    like hair and out-of-mesh clothing. This results in a minimal number of Gaussians
    being used to represent the full avatar, and reduced rendering artifacts. This
    allows us to handle the animation of small body parts such as fingers that are
    traditionally disregarded. We demonstrate the effectiveness of our approach on
    two open datasets: SnapshotPeople and X-Humans. Our method demonstrates on par
    reconstruction quality to the state-of-the-art on SnapshotPeople, while using
    less than a third of Gaussians. HAHA outperforms previous state-of-the-art on
    novel poses from X-Humans both quantitatively and qualitatively.'
  project_page: null
  paper: https://arxiv.org/pdf/2404.01053
  code: https://github.com/david-svitov/HAHA
  video: null
  tags:
  - Avatar
  - Code
  thumbnail: assets/thumbnails/shao2024haha.jpg
  publication_date: '2024-04-01T11:23:38+00:00'
- id: c2024mm3dgs
  title: 'MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth,
    and Inertial Measurements'
  authors: Lisong C. Sun, Neel P. Bhatt, Jonathan C. Liu, Zhiwen Fan, Zhangyang Wang,
    Todd E. Humphreys, Ufuk Topcu
  year: '2024'
  abstract: Simultaneous localization and mapping is essential for position tracking
    and scene understanding. 3D Gaussian-based map representations enable photorealistic
    reconstruction and real-time rendering of scenes using multiple posed cameras.
    We show for the first time that using 3D Gaussians for map representation with
    unposed camera images and inertial measurements can enable accurate SLAM. Our
    method, MM3DGS, addresses the limitations of prior neural radiance field-based
    representations by enabling faster rendering, scale awareness, and improved trajectory
    tracking. Our framework enables keyframe-based mapping and tracking utilizing
    loss functions that incorporate relative pose transformations from pre-integrated
    inertial measurements, depth estimates, and measures of photometric rendering
    quality. We also release a multi-modal dataset, UT-MM, collected from a mobile
    robot equipped with a camera and an inertial measurement unit. Experimental evaluation
    on several scenes from the dataset shows that MM3DGS achieves 3x improvement in
    tracking and 5% improvement in photometric rendering quality compared to the current
    3DGS SLAM state-of-the-art, while allowing real-time rendering of a high-resolution
    dense 3D map.
  project_page: https://vita-group.github.io/MM3DGS-SLAM/
  paper: https://arxiv.org/pdf/2404.00923
  code: https://github.com/VITA-Group/MM3DGS-SLAM
  video: https://www.youtube.com/watch?v=drf6UxehChE&feature=youtu.be
  tags:
  - Code
  - Project
  - SLAM
  - Video
  thumbnail: assets/thumbnails/c2024mm3dgs.jpg
  publication_date: '2024-04-01T04:57:41+00:00'
- id: comi20243dgsr
  title: '3DGSR: Implicit Surface Reconstruction with 3D Gaussian Splatting'
  authors: Mauro Comi, Alessio Tonioni, Max Yang, Jonathan Tremblay, Valts Blukis,
    Yijiong Lin, Nathan F. Lepora, Laurence Aitchison
  year: '2024'
  abstract: In this paper, we present an implicit surface reconstruction method with
    3D Gaussian Splatting (3DGS), namely 3DGSR, that allows for accurate 3D reconstruction
    with intricate details while inheriting the high efficiency and rendering quality
    of 3DGS. The key insight is incorporating an implicit signed distance field (SDF)
    within 3D Gaussians to enable them to be aligned and jointly optimized. First,
    we introduce a differentiable SDF-to-opacity transformation function that converts
    SDF values into corresponding Gaussians' opacities. This function connects the
    SDF and 3D Gaussians, allowing for unified optimization and enforcing surface
    constraints on the 3D Gaussians. During learning, optimizing the 3D Gaussians
    provides supervisory signals for SDF learning, enabling the reconstruction of
    intricate details. However, this only provides sparse supervisory signals to the
    SDF at locations occupied by Gaussians, which is insufficient for learning a continuous
    SDF. Then, to address this limitation, we incorporate volumetric rendering and
    align the rendered geometric attributes (depth, normal) with those derived from
    3D Gaussians. This consistency regularization introduces supervisory signals to
    locations not covered by discrete 3D Gaussians, effectively eliminating redundant
    surfaces outside the Gaussian sampling range. Our extensive experimental results
    demonstrate that our 3DGSR method enables high-quality 3D surface reconstruction
    while preserving the efficiency and rendering quality of 3DGS. Besides, our method
    competes favorably with leading surface reconstruction techniques while offering
    a more efficient learning process and much better rendering qualities.
  project_page: null
  paper: https://arxiv.org/pdf/2404.00409.pdf
  code: null
  video: null
  tags:
  - Meshing
  - Rendering
  thumbnail: assets/thumbnails/comi20243dgsr.jpg
  publication_date: '2024-03-30T16:35:38+00:00'
- id: fan2024instantsplat
  title: 'InstantSplat: Unbounded Sparse-view Pose-free Gaussian Splatting in 40 Seconds'
  authors: Zhiwen Fan, Wenyan Cong, Kairun Wen, Kevin Wang, Jian Zhang, Xinghao Ding,
    Danfei Xu, Boris Ivanovic, Marco Pavone, Georgios Pavlakos, Zhangyang Wang, Yue
    Wang
  year: '2024'
  abstract: While novel view synthesis (NVS) has made substantial progress in 3D computer
    vision, it typically requires an initial estimation of camera intrinsics and extrinsics
    from dense viewpoints. This pre-processing is usually conducted via a Structure-from-Motion
    (SfM) pipeline, a procedure that can be slow and unreliable, particularly in sparse-view
    scenarios with insufficient matched features for accurate reconstruction. In this
    work, we integrate the strengths of point-based representations (e.g., 3D Gaussian
    Splatting, 3D-GS) with end-to-end dense stereo models (DUSt3R) to tackle the complex
    yet unresolved issues in NVS under unconstrained settings, which encompasses pose-free
    and sparse view challenges. Our framework, InstantSplat, unifies dense stereo
    priors with 3D-GS to build 3D Gaussians of large-scale scenes from sparseview
    & pose-free images in less than 1 minute. Specifically, InstantSplat comprises
    a Coarse Geometric Initialization (CGI) module that swiftly establishes a preliminary
    scene structure and camera parameters across all training views, utilizing globally-aligned
    3D point maps derived from a pre-trained dense stereo pipeline. This is followed
    by the Fast 3D-Gaussian Optimization (F-3DGO) module, which jointly optimizes
    the 3D Gaussian attributes and the initialized poses with pose regularization.
    Experiments conducted on the large-scale outdoor Tanks & Temples datasets demonstrate
    that InstantSplat significantly improves SSIM (by 32%) while concurrently reducing
    Absolute Trajectory Error (ATE) by 80%. These establish InstantSplat as a viable
    solution for scenarios involving posefree and sparse-view conditions.
  project_page: https://instantsplat.github.io/
  paper: https://arxiv.org/pdf/2403.20309.pdf
  code: https://github.com/NVlabs/InstantSplat
  video: https://www.youtube.com/live/JdfrG89iPOA?si=JhoiMxrjVIh91Ws1
  tags:
  - Code
  - Project
  - Sparse
  - Video
  thumbnail: assets/thumbnails/fan2024instantsplat.jpg
  publication_date: '2024-03-29T17:29:58+00:00'
- id: comi2024snapit
  title: 'Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing
    Challenging Surfaces'
  authors: Mauro Comi, Alessio Tonioni, Max Yang, Jonathan Tremblay, Valts Blukis,
    Yijiong Lin, Nathan F. Lepora, Laurence Aitchison
  year: '2024'
  abstract: Touch and vision go hand in hand, mutually enhancing our ability to understand
    the world. From a research perspective, the problem of mixing touch and vision
    is underexplored and presents interesting challenges. To this end, we propose
    Tactile-Informed 3DGS, a novel approach that incorporates touch data (local depth
    maps) with multi-view vision data to achieve surface reconstruction and novel
    view synthesis. Our method optimises 3D Gaussian primitives to accurately model
    the object's geometry at points of contact. By creating a framework that decreases
    the transmittance at touch locations, we achieve a refined surface reconstruction,
    ensuring a uniformly smooth depth map. Touch is particularly useful when considering
    non-Lambertian objects (e.g. shiny or reflective surfaces) since contemporary
    methods tend to fail to reconstruct with fidelity specular highlights. By combining
    vision and tactile sensing, we achieve more accurate geometry reconstructions
    with fewer images than prior methods. We conduct evaluation on objects with glossy
    and reflective surfaces and demonstrate the effectiveness of our approach, offering
    significant improvements in reconstruction quality.
  project_page: https://maxyang27896.github.io/publication/gaussian_splat/
  paper: https://arxiv.org/pdf/2403.20275
  code: null
  video: null
  tags:
  - Project
  - Rendering
  thumbnail: assets/thumbnails/comi2024snapit.jpg
  publication_date: '2024-03-29T16:30:17+00:00'
- id: wu2024hgsmapping
  title: 'HGS-Mapping: Online Dense Mapping Using Hybrid Gaussian Representation in
    Urban Scenes'
  authors: Ke Wu, Kaizhao Zhang, Zhiwei Zhang, Shanshuai Yuan, Muer Tie, Julong Wei,
    Zijun Xu, Jieru Zhao, Zhongxue Gan, Wenchao Ding
  year: '2024'
  abstract: Online dense mapping of urban scenes forms a fundamental cornerstone for
    scene understanding and navigation of autonomous vehicles. Recent advancements
    in mapping methods are mainly based on NeRF, whose rendering speed is too slow
    to meet online requirements. 3D Gaussian Splatting (3DGS), with its rendering
    speed hundreds of times faster than NeRF, holds greater potential in online dense
    mapping. However, integrating 3DGS into a street-view dense mapping framework
    still faces two challenges, including incomplete reconstruction due to the absence
    of geometric information beyond the LiDAR coverage area and extensive computation
    for reconstruction in large urban scenes. To this end, we propose HGS-Mapping,
    an online dense mapping framework in unbounded large-scale scenes. To attain complete
    construction, our framework introduces Hybrid Gaussian Representation, which models
    different parts of the entire scene using Gaussians with distinct properties.
    Furthermore, we employ a hybrid Gaussian initialization mechanism and an adaptive
    update method to achieve high-fidelity and rapid reconstruction. To the best of
    our knowledge, we are the first to integrate Gaussian representation into online
    dense mapping of urban scenes. Our approach achieves SOTA reconstruction accuracy
    while only employing 66% number of Gaussians, leading to 20% faster reconstruction
    speed.
  project_page: null
  paper: https://arxiv.org/pdf/2403.20159.pdf
  code: null
  video: null
  tags:
  - Large-Scale
  thumbnail: assets/thumbnails/wu2024hgsmapping.jpg
  publication_date: '2024-03-29T13:16:05+00:00'
- id: yu2024sgd
  title: 'SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior'
  authors: Zhongrui Yu, Haoran Wang, Jinze Yang, Hanzhang Wang, Zeke Xie, Yunfeng
    Cai, Jiale Cao, Zhong Ji, Mingming Sun
  year: '2024'
  abstract: Novel View Synthesis (NVS) for street scenes play a critical role in the
    autonomous driving simulation. The current mainstream technique to achieve it
    is neural rendering, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting
    (3DGS). Although thrilling progress has been made, when handling street scenes,
    current methods struggle to maintain rendering quality at the viewpoint that deviates
    significantly from the training viewpoints. This issue stems from the sparse training
    views captured by a fixed camera on a moving vehicle. To tackle this problem,
    we propose a novel approach that enhances the capacity of 3DGS by leveraging prior
    from a Diffusion Model along with complementary multi-modal data. Specifically,
    we first fine-tune a Diffusion Model by adding images from adjacent frames as
    condition, meanwhile exploiting depth data from LiDAR point clouds to supply additional
    spatial information. Then we apply the Diffusion Model to regularize the 3DGS
    at unseen views during training. Experimental results validate the effectiveness
    of our method compared with current state-of-the-art models, and demonstrate its
    advance in rendering images from broader views.
  project_page: null
  paper: https://arxiv.org/pdf/2403.20079.pdf
  code: null
  video: null
  tags:
  - Diffusion
  - Lidar
  - Sparse
  thumbnail: assets/thumbnails/yu2024sgd.jpg
  publication_date: '2024-03-29T09:20:29+00:00'
- id: li2024hogaussian
  title: 'HO-Gaussian: Hybrid Optimization of 3D Gaussian Splatting for Urban Scenes'
  authors: Zhuopeng Li, Yilin Zhang, Chenming Wu, Jianke Zhu, Liangjun Zhang
  year: '2024'
  abstract: The rapid growth of 3D Gaussian Splatting (3DGS) has revolutionized neural
    rendering, enabling real-time production of high-quality renderings. However,
    the previous 3DGS-based methods have limitations in urban scenes due to reliance
    on initial Structure-from-Motion(SfM) points and difficulties in rendering distant,
    sky and low-texture areas. To overcome these challenges, we propose a hybrid optimization
    method named HO-Gaussian, which combines a grid-based volume with the 3DGS pipeline.
    HO-Gaussian eliminates the dependency on SfM point initialization, allowing for
    rendering of urban scenes, and incorporates the Point Densitification to enhance
    rendering quality in problematic regions during training. Furthermore, we introduce
    Gaussian Direction Encoding as an alternative for spherical harmonics in the rendering
    pipeline, which enables view-dependent color representation. To account for multi-camera
    systems, we introduce neural warping to enhance object consistency across different
    cameras. Experimental results on widely used autonomous driving datasets demonstrate
    that HO-Gaussian achieves photo-realistic rendering in real-time on multi-camera
    urban datasets.
  project_page: null
  paper: https://arxiv.org/pdf/2403.20032.pdf
  code: null
  video: null
  tags:
  - Densification
  - Misc
  thumbnail: assets/thumbnails/li2024hogaussian.jpg
  publication_date: '2024-03-29T07:58:21+00:00'
- id: zhang2024gaussiancube
  title: 'GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative
    Modeling'
  authors: Bowen Zhang, Yiji Cheng, Jiaolong Yang, Chunyu Wang, Feng Zhao, Yansong
    Tang, Dong Chen, Baining Guo
  year: '2024'
  abstract: We introduce a radiance representation that is both structured and fully
    explicit and thus greatly facilitates 3D generative modeling. Existing radiance
    representations either require an implicit feature decoder, which significantly
    degrades the modeling power of the representation, or are spatially unstructured,
    making them difficult to integrate with mainstream 3D diffusion methods. We derive
    GaussianCube by first using a novel densification-constrained Gaussian fitting
    algorithm, which yields high-accuracy fitting using a fixed number of free Gaussians,
    and then rearranging these Gaussians into a predefined voxel grid via Optimal
    Transport. Since GaussianCube is a structured grid representation, it allows us
    to use standard 3D U-Net as our backbone in diffusion modeling without elaborate
    designs. More importantly, the high-accuracy fitting of the Gaussians allows us
    to achieve a high-quality representation with orders of magnitude fewer parameters
    than previous structured representations for comparable quality, ranging from
    one to two orders of magnitude. The compactness of GaussianCube greatly eases
    the difficulty of 3D generative modeling. Extensive experiments conducted on unconditional
    and class-conditioned object generation, digital avatar creation, and text-to-3D
    synthesis all show that our model achieves state-of-the-art generation results
    both qualitatively and quantitatively, underscoring the potential of GaussianCube
    as a highly accurate and versatile radiance representation for 3D generative modeling.
  project_page: https://gaussiancube.github.io/
  paper: https://arxiv.org/pdf/2403.19655.pdf
  code: https://github.com/GaussianCube/GaussianCube
  video: https://youtu.be/3uo4Oud4cxI
  tags:
  - Code
  - Diffusion
  - Project
  - Video
  thumbnail: assets/thumbnails/zhang2024gaussiancube.jpg
  publication_date: '2024-03-28T17:59:50+00:00'
- id: song2024sags
  title: 'SA-GS: Scale-Adaptive Gaussian Splatting for Training-Free Anti-Aliasing'
  authors: Xiaowei Song, Jv Zheng, Shiran Yuan, Huan-ang Gao, Jingwei Zhao, Xiang
    He, Weihao Gu, Hao Zhao
  year: '2024'
  abstract: In this paper, we present a Scale-adaptive method for Anti-aliasing Gaussian
    Splatting (SA-GS). While the state-of-the-art method Mip-Splatting needs modifying
    the training procedure of Gaussian splatting, our method functions at test-time
    and is training-free. Specifically, SA-GS can be applied to any pretrained Gaussian
    splatting field as a plugin to significantly improve the field's anti-alising
    performance. The core technique is to apply 2D scale-adaptive filters to each
    Gaussian during test time. As pointed out by Mip-Splatting, observing Gaussians
    at different frequencies leads to mismatches between the Gaussian scales during
    training and testing. Mip-Splatting resolves this issue using 3D smoothing and
    2D Mip filters, which are unfortunately not aware of testing frequency. In this
    work, we show that a 2D scale-adaptive filter that is informed of testing frequency
    can effectively match the Gaussian scale, thus making the Gaussian primitive distribution
    remain consistent across different testing frequencies. When scale inconsistency
    is eliminated, sampling rates smaller than the scene frequency result in conventional
    jaggedness, and we propose to integrate the projected 2D Gaussian within each
    pixel during testing. This integration is actually a limiting case of super-sampling,
    which significantly improves anti-aliasing performance over vanilla Gaussian Splatting.
    Through extensive experiments using various settings and both bounded and unbounded
    scenes, we show SA-GS performs comparably with or better than Mip-Splatting. Note
    that super-sampling and integration are only effective when our scale-adaptive
    filtering is activated.
  project_page: https://kevinsong729.github.io/project-pages/SA-GS/
  paper: https://arxiv.org/pdf/2403.19615
  code: https://github.com/zsy1987/SA-GS/
  video: null
  tags:
  - Antialiasing
  - Code
  - Project
  - Rendering
  thumbnail: assets/thumbnails/song2024sags.jpg
  publication_date: '2024-03-28T17:32:58+00:00'
- id: zhang2024togs
  title: 'TOGS: Gaussian Splatting with Temporal Opacity Offset for Real-Time 4D DSA
    Rendering'
  authors: Shuai Zhang, Huangxuan Zhao, Zhenghong Zhou, Guanjun Wu, Chuansheng Zheng,
    Xinggang Wang, Wenyu Liu
  year: '2024'
  abstract: Four-dimensional Digital Subtraction Angiography (4D DSA) is a medical
    imaging technique that provides a series of 2D images captured at different stages
    and angles during the process of contrast agent filling blood vessels. It plays
    a significant role in the diagnosis of cerebrovascular diseases. Improving the
    rendering quality and speed under sparse sampling is important for observing the
    status and location of lesions. The current methods exhibit inadequate rendering
    quality in sparse views and suffer from slow rendering speed. To overcome these
    limitations, we propose TOGS, a Gaussian splatting method with opacity offset
    over time, which can effectively improve the rendering quality and speed of 4D
    DSA. We introduce an opacity offset table for each Gaussian to model the temporal
    variations in the radiance of the contrast agent. By interpolating the opacity
    offset table, the opacity variation of the Gaussian at different time points can
    be determined. This enables us to render the 2D DSA image at that specific moment.
    Additionally, we introduced a Smooth loss term in the loss function to mitigate
    overfitting issues that may arise in the model when dealing with sparse view scenarios.
    During the training phase, we randomly prune Gaussians, thereby reducing the storage
    overhead of the model. The experimental results demonstrate that compared to previous
    methods, this model achieves state-of-the-art reconstruction quality under the
    same number of training views. Additionally, it enables real-time rendering while
    maintaining low storage overhead.
  project_page: null
  paper: https://arxiv.org/pdf/2403.19586
  code: https://github.com/hustvl/TOGS
  video: null
  tags:
  - Code
  - Medicine
  thumbnail: assets/thumbnails/zhang2024togs.jpg
  publication_date: '2024-03-28T17:08:58+00:00'
- id: paliwal2024coherentgs
  title: 'CoherentGS: Sparse Novel View Synthesis with Coherent 3D Gaussians'
  authors: Avinash Paliwal, Wei Ye, Jinhui Xiong, Dmytro Kotovenko, Rakesh Ranjan,
    Vikas Chandra, Nima Khademi Kalantari
  year: '2024'
  abstract: The field of 3D reconstruction from images has rapidly evolved in the
    past few years, first with the introduction of Neural Radiance Field (NeRF) and
    more recently with 3D Gaussian Splatting (3DGS). The latter provides a significant
    edge over NeRF in terms of the training and inference speed, as well as the reconstruction
    quality. Although 3DGS works well for dense input images, the unstructured point-cloud
    like representation quickly overfits to the more challenging setup of extremely
    sparse input images (e.g., 3 images), creating a representation that appears as
    a jumble of needles from novel views. To address this issue, we propose regularized
    optimization and depth-based initialization. Our key idea is to introduce a structured
    Gaussian representation that can be controlled in 2D image space. We then constraint
    the Gaussians, in particular their position, and prevent them from moving independently
    during optimization. Specifically, we introduce single and multiview constraints
    through an implicit convolutional decoder and a total variation loss, respectively.
    With the coherency introduced to the Gaussians, we further constrain the optimization
    through a flow-based loss function. To support our regularized optimization, we
    propose an approach to initialize the Gaussians using monocular depth estimates
    at each input view. We demonstrate significant improvements compared to the state-of-the-art
    sparse-view NeRF-based approaches on a variety of scenes.
  project_page: https://people.engr.tamu.edu/nimak/Papers/CoherentGS/index.html
  paper: https://arxiv.org/pdf/2403.19495
  code: https://github.com/avinashpaliwal/CoherentGS
  video: https://www.youtube.com/watch?v=qiWdD3tOHKM
  tags:
  - Code
  - Project
  - Sparse
  - Video
  thumbnail: assets/thumbnails/paliwal2024coherentgs.jpg
  publication_date: '2024-03-28T15:27:13+00:00'
- id: shen2024gamba
  title: 'Gamba: Marry Gaussian Splatting with Mamba for single view 3D reconstruction'
  authors: Qiuhong Shen, Xuanyu Yi, Zike Wu, Pan Zhou, Hanwang Zhang, Shuicheng Yan,
    Xinchao Wang
  year: '2024'
  abstract: 'We tackle the challenge of efficiently reconstructing a 3D asset from
    a single image with growing demands for automated 3D content creation pipelines.
    Previous methods primarily rely on Score Distillation Sampling (SDS) and Neural
    Radiance Fields (NeRF). Despite their significant success, these approaches encounter
    practical limitations due to lengthy optimization and considerable memory usage.
    In this report, we introduce Gamba, an end-to-end amortized 3D reconstruction
    model from single-view images, emphasizing two main insights: (1) 3D representation:
    leveraging a large number of 3D Gaussians for an efficient 3D Gaussian splatting
    process; (2) Backbone design: introducing a Mamba-based sequential network that
    facilitates context-dependent reasoning and linear scalability with the sequence
    (token) length, accommodating a substantial number of Gaussians. Gamba incorporates
    significant advancements in data preprocessing, regularization design, and training
    methodologies. We assessed Gamba against existing optimization-based and feed-forward
    3D generation approaches using the real-world scanned OmniObject3D dataset. Here,
    Gamba demonstrates competitive generation capabilities, both qualitatively and
    quantitatively, while achieving remarkable speed, approximately 0.6 second on
    a single NVIDIA A100 GPU.'
  project_page: https://florinshen.github.io/gamba-project/
  paper: https://arxiv.org/pdf/2403.18795
  code: https://github.com/SkyworkAI/Gamba
  video: null
  tags:
  - Code
  - Feed-Forward
  - Project
  - Sparse
  thumbnail: assets/thumbnails/shen2024gamba.jpg
  publication_date: '2024-03-27T17:40:14+00:00'
- id: shao2024splatface
  title: 'SplatFace: Gaussian Splat Face Reconstruction Leveraging an Optimizable
    Surface'
  authors: Zhijing Shao, Zhaolong Wang, Zhuang Li, Duotun Wang, Xiangru Lin, Yu Zhang,
    Mingming Fan, Zeyu Wang
  year: '2024'
  abstract: We present SplatFace, a novel Gaussian splatting framework designed for
    3D human face reconstruction without reliance on accurate pre-determined geometry.
    Our method is designed to simultaneously deliver both high-quality novel view
    rendering and accurate 3D mesh reconstructions. We incorporate a generic 3D Morphable
    Model (3DMM) to provide a surface geometric structure, making it possible to reconstruct
    faces with a limited set of input images. We introduce a joint optimization strategy
    that refines both the Gaussians and the morphable surface through a synergistic
    non-rigid alignment process. A novel distance metric, splat-to-surface, is proposed
    to improve alignment by considering both the Gaussian position and covariance.
    The surface information is also utilized to incorporate a world-space densification
    process, resulting in superior reconstruction quality. Our experimental analysis
    demonstrates that the proposed method is competitive with both other Gaussian
    splatting techniques in novel view synthesis and other 3D reconstruction methods
    in producing 3D face meshes with high geometric precision.
  project_page: null
  paper: https://arxiv.org/pdf/2403.18784
  code: null
  video: null
  tags:
  - Avatar
  thumbnail: assets/thumbnails/shao2024splatface.jpg
  publication_date: '2024-03-27T17:32:04+00:00'
- id: savant2024modeling
  title: Modeling uncertainty for Gaussian Splatting
  authors: Luca Savant, Diego Valsesia, Enrico Magli
  year: '2024'
  abstract: 'We present Stochastic Gaussian Splatting (SGS): the first framework for
    uncertainty estimation using Gaussian Splatting (GS). GS recently advanced the
    novel-view synthesis field by achieving impressive reconstruction quality at a
    fraction of the computational cost of Neural Radiance Fields (NeRF). However,
    contrary to the latter, it still lacks the ability to provide information about
    the confidence associated with their outputs. To address this limitation, in this
    paper, we introduce a Variational Inference-based approach that seamlessly integrates
    uncertainty prediction into the common rendering pipeline of GS. Additionally,
    we introduce the Area Under Sparsification Error (AUSE) as a new term in the loss
    function, enabling optimization of uncertainty estimation alongside image reconstruction.
    Experimental results on the LLFF dataset demonstrate that our method outperforms
    existing approaches in terms of both image rendering quality and uncertainty estimation
    accuracy. Overall, our framework equips practitioners with valuable insights into
    the reliability of synthesized views, facilitating safer decision-making in real-world
    applications.'
  project_page: null
  paper: https://arxiv.org/pdf/2403.18476
  code: null
  video: null
  tags:
  - Uncertainty
  thumbnail: assets/thumbnails/savant2024modeling.jpg
  publication_date: '2024-03-27T11:45:08+00:00'
- id: gu2024egolifter
  title: 'EgoLifter: Open-world 3D Segmentation for Egocentric Perception'
  authors: Qiao Gu, Zhaoyang Lv, Duncan Frost, Simon Green, Julian Straub, Chris Sweeney
  year: '2024'
  abstract: In this paper we present EgoLifter, a novel system that can automatically
    segment scenes captured from egocentric sensors into a complete decomposition
    of individual 3D objects. The system is specifically designed for egocentric data
    where scenes contain hundreds of objects captured from natural (non-scanning)
    motion. EgoLifter adopts 3D Gaussians as the underlying representation of 3D scenes
    and objects and uses segmentation masks from the Segment Anything Model (SAM)
    as weak supervision to learn flexible and promptable definitions of object instances
    free of any specific object taxonomy. To handle the challenge of dynamic objects
    in ego-centric videos, we design a transient prediction module that learns to
    filter out dynamic objects in the 3D reconstruction. The result is a fully automatic
    pipeline that is able to reconstruct 3D object instances as collections of 3D
    Gaussians that collectively compose the entire scene. We created a new benchmark
    on the Aria Digital Twin dataset that quantitatively demonstrates its state-of-the-art
    performance in open-world 3D segmentation from natural egocentric input. We run
    EgoLifter on various egocentric activity datasets which shows the promise of the
    method for 3D egocentric perception at scale.
  project_page: https://egolifter.github.io/
  paper: https://arxiv.org/pdf/2403.18118.pdf
  code: https://github.com/facebookresearch/egolifter
  video: https://www.youtube.com/watch?v=dWuZyeiOXyM&feature=youtu.be
  tags:
  - Code
  - Project
  - Segmentation
  - Video
  thumbnail: assets/thumbnails/gu2024egolifter.jpg
  publication_date: '2024-03-26T21:48:27+00:00'
- id: ren2024octreegs
  title: 'Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D
    Gaussians'
  authors: Kerui Ren, Lihan Jiang, Tao Lu, Mulin Yu, Linning Xu, Zhangkai Ni, Bo Dai
  year: '2024'
  abstract: The recent 3D Gaussian splatting (3D-GS) has shown remarkable rendering
    fidelity and efficiency compared to NeRF-based neural scene representations. While
    demonstrating the potential for real-time rendering, 3D-GS encounters rendering
    bottlenecks in large scenes with complex details due to an excessive number of
    Gaussian primitives located within the viewing frustum. This limitation is particularly
    noticeable in zoom-out views and can lead to inconsistent rendering speeds in
    scenes with varying details. Moreover, it often struggles to capture the corresponding
    level of details at different scales with its heuristic density control operation.
    Inspired by the Level-of-Detail (LOD) techniques, we introduce Octree-GS, featuring
    an LOD-structured 3D Gaussian approach supporting level-of-detail decomposition
    for scene representation that contributes to the final rendering results. Our
    model dynamically selects the appropriate level from the set of multi-resolution
    anchor points, ensuring consistent rendering performance with adaptive LOD adjustments
    while maintaining high-fidelity rendering results.
  project_page: https://city-super.github.io/octree-gs/
  paper: https://arxiv.org/pdf/2403.17898
  code: https://github.com/city-super/Octree-GS
  video: null
  tags:
  - Code
  - Large-Scale
  - Project
  - Rendering
  thumbnail: assets/thumbnails/ren2024octreegs.jpg
  publication_date: '2024-03-26T17:39:36+00:00'
- id: huang20242d
  title: 2D Gaussian Splatting for Geometrically Accurate Radiance Fields
  authors: Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, Shenghua Gao
  year: '2024'
  abstract: 3D Gaussian Splatting (3DGS) has recently revolutionized radiance field
    reconstruction, achieving high quality novel view synthesis and fast rendering
    speed without baking. However, 3DGS fails to accurately represent surfaces due
    to the multi-view inconsistent nature of 3D Gaussians. We present 2D Gaussian
    Splatting (2DGS), a novel approach to model and reconstruct geometrically accurate
    radiance fields from multi-view images. Our key idea is to collapse the 3D volume
    into a set of 2D oriented planar Gaussian disks. Unlike 3D Gaussians, 2D Gaussians
    provide view-consistent geometry while modeling surfaces intrinsically. To accurately
    recover thin surfaces and achieve stable optimization, we introduce a perspective-accurate
    2D splatting process utilizing ray-splat intersection and rasterization. Additionally,
    we incorporate depth distortion and normal consistency terms to further enhance
    the quality of the reconstructions. We demonstrate that our differentiable renderer
    allows for noise-free and detailed geometry reconstruction while maintaining competitive
    appearance quality, fast training speed, and real-time rendering.
  project_page: https://surfsplatting.github.io/
  paper: https://arxiv.org/pdf/2403.17888
  code: https://github.com/hbb1/2d-gaussian-splatting
  video: https://www.youtube.com/watch?v=oaHCtB6yiKU
  tags:
  - 2DGS
  - Code
  - Meshing
  - Project
  - Video
  thumbnail: assets/thumbnails/huang20242d.jpg
  publication_date: '2024-03-26T17:21:24+00:00'
- id: turkulainen2024dnsplatter
  title: 'DN-Splatter: Depth and Normal Priors for Gaussian Splatting and Meshing'
  authors: Matias Turkulainen, Xuqian Ren, Iaroslav Melekhov, Otto Seiskari, Esa Rahtu,
    Juho Kannala
  year: '2024'
  abstract: 3D Gaussian splatting, a novel differentiable rendering technique, has
    achieved state-of-the-art novel view synthesis results with high rendering speeds
    and relatively low training times. However, its performance on scenes commonly
    seen in indoor datasets is poor due to the lack of geometric constraints during
    optimization. We extend 3D Gaussian splatting with depth and normal cues to tackle
    challenging indoor datasets and showcase techniques for efficient mesh extraction,
    an important downstream application. Specifically, we regularize the optimization
    procedure with depth information, enforce local smoothness of nearby Gaussians,
    and use the geometry of the 3D Gaussians supervised by normal cues to achieve
    better alignment with the true scene geometry. We improve depth estimation and
    novel view synthesis results over baselines and show how this simple yet effective
    regularization technique can be used to directly extract meshes from the Gaussian
    representation yielding more physically accurate reconstructions on indoor scenes.
  project_page: https://maturk.github.io/dn-splatter/
  paper: https://arxiv.org/pdf/2403.17822
  code: https://github.com/maturk/dn-splatter
  video: null
  tags:
  - Code
  - Meshing
  - Project
  thumbnail: assets/thumbnails/turkulainen2024dnsplatter.jpg
  publication_date: '2024-03-26T16:00:31+00:00'
- id: lin2024dreampolisher
  title: 'DreamPolisher: Towards High-Quality Text-to-3D Generation via Geometric
    Diffusion'
  authors: Yuanze Lin, Ronald Clark, Philip Torr
  year: '2024'
  abstract: We present DreamPolisher, a novel Gaussian Splatting based method with
    geometric guidance, tailored to learn cross-view consistency and intricate detail
    from textual descriptions. While recent progress on text-to-3D generation methods
    have been promising, prevailing methods often fail to ensure view-consistency
    and textural richness. This problem becomes particularly noticeable for methods
    that work with text input alone. To address this, we propose a two-stage Gaussian
    Splatting based approach that enforces geometric consistency among views. Initially,
    a coarse 3D generation undergoes refinement via geometric optimization. Subsequently,
    we use a ControlNet driven refiner coupled with the geometric consistency term
    to improve both texture fidelity and overall consistency of the generated 3D asset.
    Empirical evaluations across diverse textual prompts spanning various object categories
    demonstrate the efficacy of DreamPolisher in generating consistent and realistic
    3D objects, aligning closely with the semantics of the textual instructions.
  project_page: https://yuanze-lin.me/DreamPolisher_page/
  paper: https://arxiv.org/pdf/2403.17237
  code: https://github.com/yuanze-lin/DreamPolisher
  video: https://youtu.be/YJkFMIV2OyQ
  tags:
  - Code
  - Diffusion
  - Project
  - Video
  thumbnail: assets/thumbnails/lin2024dreampolisher.jpg
  publication_date: '2024-03-25T22:34:05+00:00'
- id: xu2024comp4d
  title: 'Comp4D: LLM-Guided Compositional 4D Scene Generation'
  authors: Dejia Xu, Hanwen Liang, Neel P. Bhatt, Hezhen Hu, Hanxue Liang, Konstantinos
    N. Plataniotis, Zhangyang Wang
  year: '2024'
  abstract: Recent advancements in diffusion models for 2D and 3D content creation
    have sparked a surge of interest in generating 4D content. However, the scarcity
    of 3D scene datasets constrains current methodologies to primarily object-centric
    generation. To overcome this limitation, we present Comp4D, a novel framework
    for Compositional 4D Generation. Unlike conventional methods that generate a singular
    4D representation of the entire scene, Comp4D innovatively constructs each 4D
    object within the scene separately. Utilizing Large Language Models (LLMs), the
    framework begins by decomposing an input text prompt into distinct entities and
    maps out their trajectories. It then constructs the compositional 4D scene by
    accurately positioning these objects along their designated paths. To refine the
    scene, our method employs a compositional score distillation technique guided
    by the pre-defined trajectories, utilizing pre-trained diffusion models across
    text-to-image, text-to-video, and text-to-3D domains. Extensive experiments demonstrate
    our outstanding 4D content creation capability compared to prior arts, showcasing
    superior visual quality, motion fidelity, and enhanced object interactions.
  project_page: https://vita-group.github.io/Comp4D/
  paper: https://arxiv.org/pdf/2403.16993.pdf
  code: https://github.com/VITA-Group/Comp4D
  video: https://youtu.be/9q8SV1Xf_Xw
  tags:
  - Code
  - Diffusion
  - Project
  - Video
  thumbnail: assets/thumbnails/xu2024comp4d.jpg
  publication_date: '2024-03-25T17:55:52+00:00'
- id: yu2024gsdf
  title: 'GSDF: 3DGS Meets SDF for Improved Rendering and Reconstruction'
  authors: Mulin Yu, Tao Lu, Linning Xu, Lihan Jiang, Yuanbo Xiangli, Bo Dai
  year: '2024'
  abstract: Presenting a 3D scene from multiview images remains a core and long-standing
    challenge in computer vision and computer graphics. Two main requirements lie
    in rendering and reconstruction. Notably, SOTA rendering quality is usually achieved
    with neural volumetric rendering techniques, which rely on aggregated point/primitive-wise
    color and neglect the underlying scene geometry. Learning of neural implicit surfaces
    is sparked from the success of neural rendering. Current works either constrain
    the distribution of density fields or the shape of primitives, resulting in degraded
    rendering quality and flaws on the learned scene surfaces. The efficacy of such
    methods is limited by the inherent constraints of the chosen neural representation,
    which struggles to capture fine surface details, especially for larger, more intricate
    scenes. To address these issues, we introduce GSDF, a novel dual-branch architecture
    that combines the benefits of a flexible and efficient 3D Gaussian Splatting (3DGS)
    representation with neural Signed Distance Fields (SDF). The core idea is to leverage
    and enhance the strengths of each branch while alleviating their limitation through
    mutual guidance and joint supervision. We show on diverse scenes that our design
    unlocks the potential for more accurate and detailed surface reconstructions,
    and at the meantime benefits 3DGS rendering with structures that are more aligned
    with the underlying geometry.
  project_page: https://city-super.github.io/GSDF/
  paper: https://arxiv.org/pdf/2403.16964
  code: https://github.com/city-super/GSDF
  video: null
  tags:
  - Code
  - Meshing
  - Project
  - Rendering
  thumbnail: assets/thumbnails/yu2024gsdf.jpg
  publication_date: '2024-03-25T17:22:11+00:00'
- id: wewer2024latentsplat
  title: 'latentSplat: Autoencoding Variational Gaussians for Fast Generalizable 3D
    Reconstruction'
  authors: Christopher Wewer, Kevin Raj, Eddy Ilg, Bernt Schiele, Jan Eric Lenssen
  year: '2024'
  abstract: We present latentSplat, a method to predict semantic Gaussians in a 3D
    latent space that can be splatted and decoded by a light-weight generative 2D
    architecture. Existing methods for generalizable 3D reconstruction either do not
    enable fast inference of high resolution novel views due to slow volume rendering,
    or are limited to interpolation of close input views, even in simpler settings
    with a single central object, where 360-degree generalization is possible. In
    this work, we combine a regression-based approach with a generative model, moving
    towards both of these capabilities within the same method, trained purely on readily
    available real video data. The core of our method are variational 3D Gaussians,
    a representation that efficiently encodes varying uncertainty within a latent
    space consisting of 3D feature Gaussians. From these Gaussians, specific instances
    can be sampled and rendered via efficient Gaussian splatting and a fast, generative
    decoder network. We show that latentSplat outperforms previous works in reconstruction
    quality and generalization, while being fast and scalable to high-resolution data.
  project_page: https://geometric-rl.mpi-inf.mpg.de/latentsplat/
  paper: https://arxiv.org/pdf/2403.16292.pdf
  code: https://github.com/Chrixtar/latentsplat
  video: null
  tags:
  - Feed-Forward
  - Sparse
  thumbnail: assets/thumbnails/wewer2024latentsplat.jpg
  publication_date: '2024-03-24T20:48:36+00:00'
- id: hu2024cgslam
  title: 'CG-SLAM: Efficient Dense RGB-D SLAM in a Consistent Uncertainty-aware 3D
    Gaussian Field'
  authors: Jiarui Hu, Xianhao Chen, Boyin Feng, Guanglin Li, Liangjing Yang, Hujun
    Bao, Guofeng Zhang, Zhaopeng Cui
  year: '2024'
  abstract: Recently neural radiance fields (NeRF) have been widely exploited as 3D
    representations for dense simultaneous localization and mapping (SLAM). Despite
    their notable successes in surface modeling and novel view synthesis, existing
    NeRF-based methods are hindered by their computationally intensive and time-consuming
    volume rendering pipeline. This paper presents an efficient dense RGB-D SLAM system,
    i.e., CG-SLAM, based on a novel uncertainty-aware 3D Gaussian field with high
    consistency and geometric stability. Through an in-depth analysis of Gaussian
    Splatting, we propose several techniques to construct a consistent and stable
    3D Gaussian field suitable for tracking and mapping. Additionally, a novel depth
    uncertainty model is proposed to ensure the selection of valuable Gaussian primitives
    during optimization, thereby improving tracking efficiency and accuracy. Experiments
    on various datasets demonstrate that CG-SLAM achieves superior tracking and mapping
    performance with a notable tracking speed of up to 15 Hz. We will make our source
    code publicly available.
  project_page: https://zju3dv.github.io/cg-slam/
  paper: https://arxiv.org/pdf/2403.16095
  code: null
  video: https://raw.githubusercontent.com/hjr37/open_access_assets/main/cg-slam/video/cg-slam-show.mp4
  tags:
  - Project
  - SLAM
  - Video
  thumbnail: assets/thumbnails/hu2024cgslam.jpg
  publication_date: '2024-03-24T11:19:59+00:00'
- id: zhang2024gaussian
  title: 'Gaussian in the Wild: 3D Gaussian Splatting for Unconstrained Image Collections'
  authors: Dongbin Zhang, Chuming Wang, Weitao Wang, Peihao Li, Minghan Qin, Haoqian
    Wang
  year: '2024'
  abstract: Novel view synthesis from unconstrained in-the-wild images remains a meaningful
    but challenging task. The photometric variation and transient occluders in those
    unconstrained images make it difficult to reconstruct the original scene accurately.
    Previous approaches tackle the problem by introducing a global appearance feature
    in Neural Radiance Fields (NeRF). However, in the real world, the unique appearance
    of each tiny point in a scene is determined by its independent intrinsic material
    attributes and the varying environmental impacts it receives. Inspired by this
    fact, we propose Gaussian in the wild (GS-W), a method that uses 3D Gaussian points
    to reconstruct the scene and introduces separated intrinsic and dynamic appearance
    feature for each point, capturing the unchanged scene appearance along with dynamic
    variation like illumination and weather. Additionally, an adaptive sampling strategy
    is presented to allow each Gaussian point to focus on the local and detailed information
    more effectively. We also reduce the impact of transient occluders using a 2D
    visibility map. More experiments have demonstrated better reconstruction quality
    and details of GS-W compared to previous methods, with a 1000× increase in rendering
    speed.
  project_page: https://eastbeanzhang.github.io/GS-W/
  paper: https://arxiv.org/pdf/2403.15704
  code: https://github.com/EastbeanZhang/Gaussian-Wild
  video: https://www.youtube.com/watch?v=BNIX-OmIzgo
  tags:
  - Code
  - In the Wild
  - Project
  - Rendering
  - Video
  thumbnail: assets/thumbnails/zhang2024gaussian.jpg
  publication_date: '2024-03-23T03:55:41+00:00'
- id: guo2024semantic
  title: 'Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian
    Splatting'
  authors: Jun Guo, Xiaojian Ma, Yue Fan, Huaping Liu, Qing Li
  year: '2024'
  abstract: 'Open-vocabulary 3D scene understanding presents a significant challenge
    in computer vision, withwide-ranging applications in embodied agents and augmented
    reality systems. Previous approaches haveadopted Neural Radiance Fields (NeRFs)
    to analyze 3D scenes. In this paper, we introduce SemanticGaussians, a novel open-vocabulary
    scene understanding approach based on 3D Gaussian Splatting. Our keyidea is distilling
    pre-trained 2D semantics into 3D Gaussians. We design a versatile projection approachthat
    maps various 2Dsemantic features from pre-trained image encoders into a novel
    semantic component of 3D Gaussians, withoutthe additional training required by
    NeRFs. We further build a 3D semantic network that directly predictsthe semantic
    component from raw 3D Gaussians for fast inference. We explore several applications
    ofSemantic Gaussians: semantic segmentation on ScanNet-20, where our approach
    attains a 4.2% mIoU and 4.0%mAcc improvement over prior open-vocabulary scene
    understanding counterparts; object part segmentation,sceneediting, and spatial-temporal
    segmentation with better qualitative results over 2D and 3D baselines,highlighting
    its versatility and effectiveness on supporting diverse downstream tasks.'
  project_page: https://semantic-gaussians.github.io/
  paper: https://arxiv.org/pdf/2403.15624
  code: https://github.com/sharinka0715/semantic-gaussians
  video: null
  tags:
  - Code
  - Language Embedding
  - Project
  - Segmentation
  thumbnail: assets/thumbnails/guo2024semantic.jpg
  publication_date: '2024-03-22T21:28:19+00:00'
- id: zhang2024pixelgs
  title: 'Pixel-GS: Density Control with Pixel-aware Gradient for 3D Gaussian Splatting'
  authors: Zheng Zhang, Wenbo Hu, Yixing Lao, Tong He, Hengshuang Zhao
  year: '2024'
  abstract: 3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis
    results while advancing real-time rendering performance. However, it relies heavily
    on the quality of the initial point cloud, resulting in blurring and needle-like
    artifacts in areas with insufficient initializing points. This is mainly attributed
    to the point cloud growth condition in 3DGS that only considers the average gradient
    magnitude of points from observable views, thereby failing to grow for large Gaussians
    that are observable for many viewpoints while many of them are only covered in
    the boundaries. To this end, we propose a novel method, named Pixel-GS, to take
    into account the number of pixels covered by the Gaussian in each view during
    the computation of the growth condition. We regard the covered pixel numbers as
    the weights to dynamically average the gradients from different views, such that
    the growth of large Gaussians can be prompted. As a result, points within the
    areas with insufficient initializing points can be grown more effectively, leading
    to a more accurate and detailed reconstruction. In addition, we propose a simple
    yet effective strategy to scale the gradient field according to the distance to
    the camera, to suppress the growth of floaters near the camera. Extensive experiments
    both qualitatively and quantitatively demonstrate that our method achieves state-of-the-art
    rendering quality while maintaining real-time rendering speed, on the challenging
    Mip-NeRF 360 and Tanks & Temples datasets.
  project_page: https://pixelgs.github.io/
  paper: https://arxiv.org/pdf/2403.15530.pdf
  code: https://github.com/zhengzhang01/Pixel-GS
  video: null
  tags:
  - Code
  - Densification
  - Project
  - Rendering
  thumbnail: assets/thumbnails/zhang2024pixelgs.jpg
  publication_date: '2024-03-22T17:59:21+00:00'
- id: wang2024endogslam
  title: 'EndoGSLAM: Real-Time Dense Reconstruction and Tracking in Endoscopic Surgeries
    using Gaussian Splatting'
  authors: Kailing Wang, Chen Yang, Yuehao Wang, Sikuang Li, Yan Wang, Qi Dou, Xiaokang
    Yang, Wei Shen
  year: '2024'
  abstract: Precise camera tracking, high-fidelity 3D tissue reconstruction, and real-time
    online visualization are critical for intrabody medical imaging devices such as
    endoscopes and capsule robots. However, existing SLAM (Simultaneous Localization
    and Mapping) methods often struggle to achieve both complete high-quality surgical
    field reconstruction and efficient computation, restricting their intraoperative
    applications among endoscopic surgeries. In this paper, we introduce EndoGSLAM,
    an efficient SLAM approach for endoscopic surgeries, which integrates streamlined
    Gaussian representation and differentiable rasterization to facilitate over 100
    fps rendering speed during online camera tracking and tissue reconstructing. Extensive
    experiments show that EndoGSLAM achieves a better trade-off between intraoperative
    availability and reconstruction quality than traditional or neural SLAM approaches,
    showing tremendous potential for endoscopic surgeries
  project_page: https://endogslam.loping151.com/
  paper: https://arxiv.org/pdf/2403.15124.pdf
  code: https://github.com/endogslam/EndoGSLAM
  video: https://loping151.github.io/endogslam/static/video/3_1.mp4
  tags:
  - Code
  - Medicine
  - Project
  - SLAM
  - Video
  thumbnail: assets/thumbnails/wang2024endogslam.jpg
  publication_date: '2024-03-22T11:27:43+00:00'
- id: zeng2024stag4d
  title: 'STAG4D: Spatial-Temporal Anchored Generative 4D Gaussians'
  authors: Yifei Zeng, Yanqin Jiang, Siyu Zhu, Yuanxun Lu, Youtian Lin, Hao Zhu, Weiming
    Hu, Xun Cao, Yao Yao
  year: '2024'
  abstract: Recent progress in pre-trained diffusion models and 3D generation have
    spurred interest in 4D content creation. However, achieving high-fidelity 4D generation
    with spatial-temporal consistency remains a challenge. In this work, we propose
    STAG4D, a novel framework that combines pre-trained diffusion models with dynamic
    3D Gaussian splatting for high-fidelity 4D generation. Drawing inspiration from
    3D generation techniques, we utilize a multi-view diffusion model to initialize
    multi-view images anchoring on the input video frames, where the video can be
    either real-world captured or generated by a video diffusion model. To ensure
    the temporal consistency of the multi-view sequence initialization, we introduce
    a simple yet effective fusion strategy to leverage the first frame as a temporal
    anchor in the self-attention computation. With the almost consistent multi-view
    sequences, we then apply the score distillation sampling to optimize the 4D Gaussian
    point cloud. The 4D Gaussian spatting is specially crafted for the generation
    task, where an adaptive densification strategy is proposed to mitigate the unstable
    Gaussian gradient for robust optimization. Notably, the proposed pipeline does
    not require any pre-training or fine-tuning of diffusion networks, offering a
    more accessible and practical solution for the 4D generation task. Extensive experiments
    demonstrate that our method outperforms prior 4D generation works in rendering
    quality, spatial-temporal consistency, and generation robustness, setting a new
    state-of-the-art for 4D generation from diverse inputs, including text, image,
    and video.
  project_page: https://nju-3dv.github.io/projects/STAG4D/
  paper: https://arxiv.org/pdf/2403.14939
  code: https://github.com/zeng-yifei/STAG4D
  video: https://www.youtube.com/watch?v=YJkFMIV2OyQ
  tags:
  - Code
  - Diffusion
  - Project
  - Video
  thumbnail: assets/thumbnails/zeng2024stag4d.jpg
  publication_date: '2024-03-22T04:16:33+00:00'
- id: chen2024mvsplat
  title: 'MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images'
  authors: Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys,
    Andreas Geiger, Tat-Jen Cham, Jianfei Cai
  year: '2024'
  abstract: We propose MVSplat, an efficient feed-forward 3D Gaussian Splatting model
    learned from sparse multi-view images. To accurately localize the Gaussian centers,
    we propose to build a cost volume representation via plane sweeping in the 3D
    space, where the cross-view feature similarities stored in the cost volume can
    provide valuable geometry cues to the estimation of depth. We learn the Gaussian
    primitives' opacities, covariances, and spherical harmonics coefficients jointly
    with the Gaussian centers while only relying on photometric supervision. We demonstrate
    the importance of the cost volume representation in learning feed-forward Gaussian
    Splatting models via extensive experimental evaluations. On the large-scale RealEstate10K
    and ACID benchmarks, our model achieves state-of-the-art performance with the
    fastest feed-forward inference speed (22 fps). Compared to the latest state-of-the-art
    method pixelSplat, our model uses 10× fewer parameters and infers more than 2×
    faster while providing higher appearance and geometry quality as well as better
    cross-dataset generalization.
  project_page: https://donydchen.github.io/mvsplat/
  paper: https://arxiv.org/pdf/2403.14627
  code: https://github.com/donydchen/mvsplat
  video: null
  tags:
  - Code
  - Feed-Forward
  - Project
  - Sparse
  thumbnail: assets/thumbnails/chen2024mvsplat.jpg
  publication_date: '2024-03-21T17:59:58+00:00'
- id: xu2024grm
  title: 'GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction
    and Generation'
  authors: Yinghao Xu, Zifan Shi, Wang Yifan, Hansheng Chen, Ceyuan Yang, Sida Peng,
    Yujun Shen, Gordon Wetzstein
  year: '2024'
  abstract: We introduce GRM, a large-scale reconstructor capable of recovering a
    3D asset from sparse-view images in around 0.1s. GRM is a feed-forward transformer-based
    model that efficiently incorporates multi-view information to translate the input
    pixels into pixel-aligned Gaussians, which are unprojected to create a set of
    densely distributed 3D Gaussians representing a scene. Together, our transformer
    architecture and the use of 3D Gaussians unlock a scalable and efficient reconstruction
    framework. Extensive experimental results demonstrate the superiority of our method
    over alternatives regarding both reconstruction quality and efficiency. We also
    showcase the potential of GRM in generative tasks, i.e., text-to-3D and image-to-3D,
    by integrating it with existing multi-view diffusion models.
  project_page: https://justimyhxu.github.io/projects/grm/
  paper: https://arxiv.org/pdf/2403.14621.pdf
  code: null
  video: null
  tags:
  - Project
  - Sparse
  thumbnail: assets/thumbnails/xu2024grm.jpg
  publication_date: '2024-03-21T17:59:34+00:00'
- id: guédon2024gaussian
  title: 'Gaussian Frosting: Editable Complex Radiance Fields with Real-Time Rendering'
  authors: Antoine Guédon, Vincent Lepetit
  year: '2024'
  abstract: We propose Gaussian Frosting, a novel mesh-based representation for high-quality
    rendering and editing of complex 3D effects in real-time. Our approach builds
    on the recent 3D Gaussian Splatting framework, which optimizes a set of 3D Gaussians
    to approximate a radiance field from images. We propose first extracting a base
    mesh from Gaussians during optimization, then building and refining an adaptive
    layer of Gaussians with a variable thickness around the mesh to better capture
    the fine details and volumetric effects near the surface, such as hair or grass.
    We call this layer Gaussian Frosting, as it resembles a coating of frosting on
    a cake. The fuzzier the material, the thicker the frosting. We also introduce
    a parameterization of the Gaussians to enforce them to stay inside the frosting
    layer and automatically adjust their parameters when deforming, rescaling, editing
    or animating the mesh. Our representation allows for efficient rendering using
    Gaussian splatting, as well as editing and animation by modifying the base mesh.
    We demonstrate the effectiveness of our method on various synthetic and real scenes,
    and show that it outperforms existing surface-based approaches. We will release
    our code and a web-based viewer as additional contributions.
  project_page: https://anttwo.github.io/frosting/
  paper: https://arxiv.org/pdf/2403.14554
  code: https://github.com/Anttwo/Frosting
  video: https://youtu.be/h7LeWq8sG78
  tags:
  - Code
  - Dynamic
  - Editing
  - Meshing
  - Project
  - Video
  thumbnail: assets/thumbnails/guédon2024gaussian.jpg
  publication_date: '2024-03-21T16:53:03+00:00'
- id: chen2024hac
  title: 'HAC: Hash-grid Assisted Context for 3D Gaussian Splatting Compression'
  authors: Yihang Chen, Qianyi Wu, Weiyao Lin, Mehrtash Harandi, Jianfei Cai
  year: '2024'
  abstract: '3D Gaussian Splatting (3DGS) has emerged as a promising framework for
    novel view synthesis, boasting rapid rendering speed with high fidelity. However,
    the substantial Gaussians and their associated attributes necessitate effective
    compression techniques. Nevertheless, the sparse and unorganized nature of the
    point cloud of Gaussians (or anchors in our paper) presents challenges for compression.
    To address this, we make use of the relations between the unorganized anchors
    and the structured hash grid, leveraging their mutual information for context
    modeling, and propose a Hash-grid Assisted Context (HAC) framework for highly
    compact 3DGS representation. Our approach introduces a binary hash grid to establish
    continuous spatial consistencies, allowing us to unveil the inherent spatial relations
    of anchors through a carefully designed context model. To facilitate entropy coding,
    we utilize Gaussian distributions to accurately estimate the probability of each
    quantized attribute, where an adaptive quantization module is proposed to enable
    high-precision quantization of these attributes for improved fidelity restoration.
    Additionally, we incorporate an adaptive masking strategy to eliminate invalid
    Gaussians and anchors. Importantly, our work is the pioneer to explore context-based
    compression for 3DGS representation, resulting in a remarkable size reduction
    of over $75\times$ compared to vanilla 3DGS, while simultaneously improving fidelity,
    and achieving over $11\times$ size reduction over SOTA 3DGS compression approach
    Scaffold-GS.

    '
  project_page: https://yihangchen-ee.github.io/project_hac/
  paper: https://arxiv.org/pdf/2403.14530.pdf
  code: https://github.com/YihangChen-ee/HAC
  video: null
  tags:
  - Code
  - Compression
  - Project
  thumbnail: assets/thumbnails/chen2024hac.jpg
  publication_date: '2024-03-21T16:28:58+00:00'
- id: kim2024synctweedies
  title: 'SyncTweedies: A General Generative Framework Based on Synchronized Diffusions'
  authors: Jaihoon Kim, Juil Koo, Kyeongmin Yeo, Minhyuk Sung
  year: '2024'
  abstract: 'We introduce a general framework for generating diverse visual content,
    including ambiguous images, panorama images, mesh textures, and Gaussian splat
    textures, by synchronizing multiple diffusion processes. We present exhaustive
    investigation into all possible scenarios for synchronizing multiple diffusion
    processes through a canonical space and analyze their characteristics across applications.
    In doing so, we reveal a previously unexplored case: averaging the outputs of
    Tweedie''s formula while conducting denoising in multiple instance spaces. This
    case also provides the best quality with the widest applicability to downstream
    tasks. We name this case SyncTweedies. In our experiments generating visual content
    aforementioned, we demonstrate the superior quality of generation by SyncTweedies
    compared to other synchronization methods, optimization-based and iterative-update-based
    methods.'
  project_page: https://synctweedies.github.io/
  paper: https://arxiv.org/pdf/2403.14370
  code: https://github.com/KAIST-Visual-AI-Group/SyncTweedies
  video: null
  tags:
  - Code
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/kim2024synctweedies.jpg
  publication_date: '2024-03-21T12:57:30+00:00'
- id: fang2024minisplatting
  title: 'Mini-Splatting: Representing Scenes with a Constrained Number of Gaussians'
  authors: Guangchi Fang, Bing Wang
  year: '2024'
  abstract: In this study, we explore the challenge of efficiently representing scenes
    with a constrained number of Gaussians. Our analysis shifts from traditional graphics
    and 2D computer vision to the perspective of point clouds, highlighting the inefficient
    spatial distribution of Gaussian representation as a key limitation in model performance.
    To address this, we introduce strategies for densification including blur split
    and depth reinitialization, and simplification through Gaussian binarization and
    sampling. These techniques reorganize the spatial positions of the Gaussians,
    resulting in significant improvements across various datasets and benchmarks in
    terms of rendering quality, resource consumption, and storage compression. Our
    proposed Mini-Splatting method integrates seamlessly with the original rasterization
    pipeline, providing a strong baseline for future research in Gaussian-Splatting-based
    works.
  project_page: null
  paper: https://arxiv.org/pdf/2403.14166.pdf
  code: https://github.com/fatPeter/mini-splatting
  video: null
  tags:
  - Code
  - Densification
  - Rendering
  thumbnail: assets/thumbnails/fang2024minisplatting.jpg
  publication_date: '2024-03-21T06:34:46+00:00'
- id: niemeyer2024radsplat
  title: 'RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time
    Rendering with 900+ FPS'
  authors: Michael Niemeyer, Fabian Manhardt, Marie-Julie Rakotosaona, Michael Oechsle,
    Daniel Duckworth, Rama Gosula, Keisuke Tateno, John Bates, Dominik Kaeser, Federico
    Tombari
  year: '2024'
  abstract: Recent advances in view synthesis and real-time rendering have achieved
    photorealistic quality at impressive rendering speeds. While Radiance Field-based
    methods achieve state-of-the-art quality in challenging scenarios such as in-the-wild
    captures and large-scale scenes, they often suffer from excessively high compute
    requirements linked to volumetric rendering. Gaussian Splatting-based methods,
    on the other hand, rely on rasterization and naturally achieve real-time rendering
    but suffer from brittle optimization heuristics that underperform on more challenging
    scenes. In this work, we present RadSplat, a lightweight method for robust real-time
    rendering of complex scenes. Our main contributions are threefold. First, we use
    radiance fields as a prior and supervision signal for optimizing point-based scene
    representations, leading to improved quality and more robust optimization. Next,
    we develop a novel pruning technique reducing the overall point count while maintaining
    high quality, leading to smaller and more compact scene representations with faster
    inference speeds. Finally, we propose a novel test-time filtering approach that
    further accelerates rendering and allows to scale to larger, house-sized scenes.
    We find that our method enables state-of-the-art synthesis of complex captures
    at 900+ FPS.
  project_page: https://m-niemeyer.github.io/radsplat/
  paper: https://arxiv.org/pdf/2403.13806.pdf
  code: null
  video: null
  tags:
  - Densification
  - Misc
  - Project
  - Rendering
  thumbnail: assets/thumbnails/niemeyer2024radsplat.jpg
  publication_date: '2024-03-20T17:59:55+00:00'
- id: seiskari2024gaussian
  title: 'Gaussian Splatting on the Move: Blur and Rolling Shutter Compensation for
    Natural Camera Motion'
  authors: Otto Seiskari, Jerry Ylilammi, Valtteri Kaatrasalo, Pekka Rantalankila,
    Matias Turkulainen, Juho Kannala, Esa Rahtu, Arno Solin
  year: '2024'
  abstract: High-quality scene reconstruction and novel view synthesis based on Gaussian
    Splatting (3DGS) typically require steady, high-quality photographs, often impractical
    to capture with handheld cameras. We present a method that adapts to camera motion
    and allows high-quality scene reconstruction with handheld video data suffering
    from motion blur and rolling shutter distortion. Our approach is based on detailed
    modelling of the physical image formation process and utilizes velocities estimated
    using visual-inertial odometry (VIO). Camera poses are considered non-static during
    the exposure time of a single image frame and camera poses are further optimized
    in the reconstruction process. We formulate a differentiable rendering pipeline
    that leverages screen space approximation to efficiently incorporate rolling-shutter
    and motion blur effects into the 3DGS framework. Our results with both synthetic
    and real data demonstrate superior performance in mitigating camera motion over
    existing methods, thereby advancing 3DGS in naturalistic settings.
  project_page: https://spectacularai.github.io/3dgs-deblur/
  paper: https://arxiv.org/pdf/2403.13327.pdf
  code: https://github.com/SpectacularAI/3dgs-deblur
  video: https://www.youtube.com/watch?v=GwhNerXMLd4&feature=youtu.be
  tags:
  - Code
  - Deblurring
  - Project
  - Rendering
  - Video
  thumbnail: assets/thumbnails/seiskari2024gaussian.jpg
  publication_date: '2024-03-20T06:19:41+00:00'
- id: he2024gvgen
  title: 'GVGEN: Text-to-3D Generation with Volumetric Representation'
  authors: Xianglong He, Junyi Chen, Sida Peng, Di Huang, Yangguang Li, Xiaoshui Huang,
    Chun Yuan, Wanli Ouyang, Tong He
  year: '2024'
  abstract: In recent years, 3D Gaussian splatting has emerged as a powerful technique
    for 3D reconstruction and generation, known for its fast and high-quality rendering
    capabilities. To address these shortcomings, this paper introduces a novel diffusion-based
    framework, GVGEN, designed to efficiently generate 3D Gaussian representations
    from text input. We propose two innovative techniques:(1) Structured Volumetric
    Representation. We first arrange disorganized 3D Gaussian points as a structured
    form GaussianVolume. This transformation allows the capture of intricate texture
    details within a volume composed of a fixed number of Gaussians. To better optimize
    the representation of these details, we propose a unique pruning and densifying
    method named the Candidate Pool Strategy, enhancing detail fidelity through selective
    optimization. (2) Coarse-to-fine Generation Pipeline. To simplify the generation
    of GaussianVolume and empower the model to generate instances with detailed 3D
    geometry, we propose a coarse-to-fine pipeline. It initially constructs a basic
    geometric structure, followed by the prediction of complete Gaussian attributes.
    Our framework, GVGEN, demonstrates superior performance in qualitative and quantitative
    assessments compared to existing 3D generation methods. Simultaneously, it maintains
    a fast generation speed (∼7 seconds), effectively striking a balance between quality
    and efficiency.
  project_page: https://gvgen.github.io/
  paper: https://arxiv.org/pdf/2403.12957
  code: https://github.com/GVGEN/GVGEN
  video: null
  tags:
  - Code
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/he2024gvgen.jpg
  publication_date: '2024-03-19T17:57:52+00:00'
- id: zhou2023hugs
  title: 'HUGS: Holistic Urban 3D Scene Understanding via Gaussian Splatting'
  authors: Hongyu Zhou, Jiahao Shao, Lu Xu, Dongfeng Bai, Weichao Qiu, Bingbing Liu,
    Yue Wang, Andreas Geiger, Yiyi Liao
  year: '2023'
  abstract: Holistic understanding of urban scenes based on RGB images is a challenging
    yet important problem. It encompasses understanding both the geometry and appearance
    to enable novel view synthesis, parsing semantic labels, and tracking moving objects.
    Despite considerable progress, existing approaches often focus on specific aspects
    of this task and require additional inputs such as LiDAR scans or manually annotated
    3D bounding boxes. In this paper, we introduce a novel pipeline that utilizes
    3D Gaussian Splatting for holistic urban scene understanding. Our main idea involves
    the joint optimization of geometry, appearance, semantics, and motion using a
    combination of static and dynamic 3D Gaussians, where moving object poses are
    regularized via physical constraints. Our approach offers the ability to render
    new viewpoints in real-time, yielding 2D and 3D semantic information with high
    accuracy, and reconstruct dynamic scenes, even in scenarios where 3D bounding
    box detection are highly noisy. Experimental results on KITTI, KITTI-360, and
    Virtual KITTI 2 demonstrate the effectiveness of our approach.
  project_page: https://xdimlab.github.io/hugs_website/
  paper: https://arxiv.org/pdf/2403.12722.pdf
  code: https://github.com/hyzhou404/HUGS
  video: null
  tags:
  - Autonomous Driving
  - Code
  - Project
  thumbnail: assets/thumbnails/zhou2023hugs.jpg
  publication_date: '2024-03-19T13:39:05+00:00'
- id: ha2024rgbd
  title: RGBD GS-ICP SLAM
  authors: Seongbo Ha, Jiung Yeon, Hyeonwoo Yu
  year: '2024'
  abstract: Simultaneous Localization and Mapping (SLAM) with dense representation
    plays a key role in robotics, Virtual Reality (VR), and Augmented Reality (AR)
    applications. Recent advancements in dense representation SLAM have highlighted
    the potential of leveraging neural scene representation and 3D Gaussian representation
    for high-fidelity spatial representation. In this paper, we propose a novel dense
    representation SLAM approach with a fusion of Generalized Iterative Closest Point
    (G-ICP) and 3D Gaussian Splatting (3DGS). In contrast to existing methods, we
    utilize a single Gaussian map for both tracking and mapping, resulting in mutual
    benefits. Through the exchange of covariances between tracking and mapping processes
    with scale alignment techniques, we minimize redundant computations and achieve
    an efficient system. Additionally, we enhance tracking accuracy and mapping quality
    through our keyframe selection methods. Experimental results demonstrate the effectiveness
    of our approach, showing an incredibly fast speed up to 107 FPS (for the entire
    system) and superior quality of the reconstructed map.
  project_page: null
  paper: https://arxiv.org/pdf/2403.12550.pdf
  code: https://github.com/Lab-of-AI-and-Robotics/GS_ICP_SLAM
  video: https://www.youtube.com/watch?v=e-bHh_uMMxE
  tags:
  - Code
  - SLAM
  - Video
  thumbnail: assets/thumbnails/ha2024rgbd.jpg
  publication_date: '2024-03-19T08:49:48+00:00'
- id: sun2024highfidelity
  title: High-Fidelity SLAM Using Gaussian Splatting with Rendering-Guided Densification
    and Regularized Optimization
  authors: Shuo Sun, Malcolm Mielle, Achim J. Lilienthal, Martin Magnusson
  year: '2024'
  abstract: We propose a dense RGBD SLAM system based on 3D Gaussian Splatting that
    provides metrically accurate pose tracking and visually realistic reconstruction.
    To this end, we first propose a Gaussian densification strategy based on the rendering
    loss to map unobserved areas and refine reobserved areas. Second, we introduce
    extra regularization parameters to alleviate the forgetting problem in the continuous
    mapping problem, where parameters tend to overfit the latest frame and result
    in decreasing rendering quality for previous frames. Both mapping and tracking
    are performed with Gaussian parameters by minimizing re-rendering loss in a differentiable
    way. Compared to recent neural and concurrently developed gaussian splatting RGBD
    SLAM baselines, our method achieves state-of-the-art results on the synthetic
    dataset Replica and competitive results on the real-world dataset TUM.
  project_page: null
  paper: https://arxiv.org/pdf/2403.12535.pdf
  code: null
  video: null
  tags:
  - SLAM
  thumbnail: assets/thumbnails/sun2024highfidelity.jpg
  publication_date: '2024-03-19T08:19:53+00:00'
- id: gao2024gaussianflow
  title: 'GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation'
  authors: Quankai Gao, Qiangeng Xu, Zhe Cao, Ben Mildenhall, Wenchao Ma, Le Chen,
    Danhang Tang, Ulrich Neumann
  year: '2024'
  abstract: Creating 4D fields of Gaussian Splatting from images or videos is a challenging
    task due to its under-constrained nature. While the optimization can draw photometric
    reference from the input videos or be regulated by generative models, directly
    supervising Gaussian motions remains underexplored. In this paper, we introduce
    a novel concept, Gaussian flow, which connects the dynamics of 3D Gaussians and
    pixel velocities between consecutive frames. The Gaussian flow can be efficiently
    obtained by splatting Gaussian dynamics into the image space. This differentiable
    process enables direct dynamic supervision from optical flow. Our method significantly
    benefits 4D dynamic content generation and 4D novel view synthesis with Gaussian
    Splatting, especially for contents with rich motions that are hard to be handled
    by existing methods. The common color drifting issue that happens in 4D generation
    is also resolved with improved Guassian dynamics. Superior visual quality on extensive
    experiments demonstrates our method's effectiveness. Quantitative and qualitative
    evaluations show that our method achieves state-of-the-art results on both tasks
    of 4D generation and 4D novel view synthesis.
  project_page: https://zerg-overmind.github.io/GaussianFlow.github.io/
  paper: https://arxiv.org/pdf/2403.12365
  code: null
  video: https://www.youtube.com/watch?v=0qRcjTw7-YU
  tags:
  - Dynamic
  - Project
  - Video
  thumbnail: assets/thumbnails/gao2024gaussianflow.jpg
  publication_date: '2024-03-19T02:22:21+00:00'
- id: wang2024reinforcement
  title: Reinforcement Learning with Generalizable Gaussian Splatting
  authors: Jiaxu Wang, Qiang Zhang, Jingkai Sun, Jiahang Cao, Yecheng Shao, Renjing
    Xu
  year: '2024'
  abstract: An excellent representation is crucial for reinforcement learning (RL)
    performance, especially in vision-based reinforcement learning tasks. The quality
    of the environment representation directly influences the achievement of the learning
    task. Previous vision-based RL typically uses explicit or implicit ways to represent
    environments, such as images, points, voxels, and neural radiance fields. However,
    these representations contain several drawbacks. They cannot either describe complex
    local geometries or generalize well to unseen scenes, or require precise foreground
    masks. Moreover, these implicit neural representations are akin to a ``black box",
    significantly hindering interpretability. 3D Gaussian Splatting (3DGS), with its
    explicit scene representation and differentiable rendering nature, is considered
    a revolutionary change for reconstruction and representation methods. In this
    paper, we propose a novel Generalizable Gaussian Splatting framework to be the
    representation of RL tasks, called GSRL. Through validation in the RoboMimic environment,
    our method achieves better results than other baselines in multiple tasks, improving
    the performance by 10%, 44%, and 15% compared with baselines on the hardest task.
    This work is the first attempt to leverage generalizable 3DGS as a representation
    for RL.
  project_page: null
  paper: https://arxiv.org/pdf/2404.07950
  code: null
  video: null
  tags:
  - Misc
  - Robotics
  thumbnail: assets/thumbnails/wang2024reinforcement.jpg
  publication_date: '2024-03-18T16:50:23+00:00'
- id: wang2024viewconsistent
  title: View-Consistent 3D Editing with Gaussian Splatting
  authors: Yuxuan Wang, Xuanyu Yi, Zike Wu, Na Zhao, Long Chen, Hanwang Zhang
  year: '2024'
  abstract: 'The advent of 3D Gaussian Splatting (3DGS) has revolutionized 3D editing,
    offering efficient, high-fidelity rendering and enabling precise local manipulations.
    Currently, diffusion-based 2D editing models are harnessed to modify multi-view
    rendered images, which then guide the editing of 3DGS models. However, this approach
    faces a critical issue of multi-view inconsistency, where the guidance images
    exhibit significant discrepancies across views, leading to mode collapse and visual
    artifacts of 3DGS. To this end, we introduce View-consistent Editing (VcEdit),
    a novel framework that seamlessly incorporates 3DGS into image editing processes,
    ensuring multi-view consistency in edited guidance images and effectively mitigating
    mode collapse issues. VcEdit employs two innovative consistency modules: the Cross-attention
    Consistency Module and the Editing Consistency Module, both designed to reduce
    inconsistencies in edited images. By incorporating these consistency modules into
    an iterative pattern, VcEdit proficiently resolves the issue of multi-view inconsistency,
    facilitating high-quality 3DGS editing across a diverse range of scenes.'
  project_page: https://vcedit.github.io/
  paper: https://arxiv.org/pdf/2403.11868.pdf
  code: null
  video: null
  tags:
  - Editing
  - Project
  thumbnail: assets/thumbnails/wang2024viewconsistent.jpg
  publication_date: '2024-03-18T15:22:09+00:00'
- id: zhao2024badgaussians
  title: 'BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting'
  authors: Lingzhe Zhao, Peng Wang, Peidong Liu
  year: '2024'
  abstract: While neural rendering has demonstrated impressive capabilities in 3D
    scene reconstruction and novel view synthesis, it heavily relies on high-quality
    sharp images and accurate camera poses. Numerous approaches have been proposed
    to train Neural Radiance Fields (NeRF) with motion-blurred images, commonly encountered
    in real-world scenarios such as low-light or long-exposure conditions. However,
    the implicit representation of NeRF struggles to accurately recover intricate
    details from severely motion-blurred images and cannot achieve real-time rendering.
    In contrast, recent advancements in 3D Gaussian Splatting achieve high-quality
    3D scene reconstruction and real-time rendering by explicitly optimizing point
    clouds into 3D Gaussians. In this paper, we introduce a novel approach, named
    BAD-Gaussians (Bundle Adjusted Deblur Gaussian Splatting), which leverages explicit
    Gaussian representation and handles severe motion-blurred images with inaccurate
    camera poses to achieve high-quality scene reconstruction. Our method models the
    physical image formation process of motion-blurred images and jointly learns the
    parameters of Gaussians while recovering camera motion trajectories during exposure
    time. In our experiments, we demonstrate that BAD-Gaussians not only achieves
    superior rendering quality compared to previous state-of-the-art deblur neural
    rendering methods on both synthetic and real datasets but also enables real-time
    rendering capabilities.
  project_page: https://lingzhezhao.github.io/BAD-Gaussians/
  paper: https://arxiv.org/pdf/2403.11831.pdf
  code: https://github.com/WU-CVGL/BAD-Gaussians/
  video: null
  tags:
  - Code
  - Deblurring
  - Project
  - Rendering
  thumbnail: assets/thumbnails/zhao2024badgaussians.jpg
  publication_date: '2024-03-18T14:43:04+00:00'
- id: ji2024nedsslam
  title: 'NEDS-SLAM: A Novel Neural Explicit Dense Semantic SLAM Framework using 3D
    Gaussian Splatting'
  authors: Yiming Ji, Yang Liu, Guanghu Xie, Boyu Ma, Zongwu Xie
  year: '2024'
  abstract: We propose NEDS-SLAM, an Explicit Dense semantic SLAM system based on
    3D Gaussian representation, that enables robust 3D semantic mapping, accurate
    camera tracking, and high-quality rendering in real-time. In the system, we propose
    a Spatially Consistent Feature Fusion model to reduce the effect of erroneous
    estimates from pre-trained segmentation head on semantic reconstruction, achieving
    robust 3D semantic Gaussian mapping. Additionally, we employ a lightweight encoder-decoder
    to compress the high-dimensional semantic features into a compact 3D Gaussian
    representation, mitigating the burden of excessive memory consumption. Furthermore,
    we leverage the advantage of 3D Gaussian splatting, which enables efficient and
    differentiable novel view rendering, and propose a Virtual Camera View Pruning
    method to eliminate outlier GS points, thereby effectively enhancing the quality
    of scene representations. Our NEDS-SLAM method demonstrates competitive performance
    over existing dense semantic SLAM methods in terms of mapping and tracking accuracy
    on Replica and ScanNet datasets, while also showing excellent capabilities in
    3D dense semantic mapping.
  project_page: null
  paper: https://arxiv.org/pdf/2403.11679.pdf
  code: null
  video: null
  tags:
  - SLAM
  thumbnail: assets/thumbnails/ji2024nedsslam.jpg
  publication_date: '2024-03-18T11:31:03+00:00'
- id: lei2024gaussnav
  title: 'GaussNav: Gaussian Splatting for Visual Navigation'
  authors: Xiaohan Lei, Min Wang, Wengang Zhou, Houqiang Li
  year: '2024'
  abstract: In embodied vision, Instance ImageGoal Navigation (IIN) requires an agent
    to locate a specific object depicted in a goal image within an unexplored environment.
    The primary difficulty of IIN stems from the necessity of recognizing the target
    object across varying viewpoints and rejecting potential distractors. Existing
    map-based navigation methods largely adopt the representation form of Bird's Eye
    View (BEV) maps, which, however, lack the representation of detailed textures
    in a scene. To address the above issues, we propose a new Gaussian Splatting Navigation
    (abbreviated as GaussNav) framework for IIN task, which constructs a novel map
    representation based on 3D Gaussian Splatting (3DGS). The proposed framework enables
    the agent to not only memorize the geometry and semantic information of the scene,
    but also retain the textural features of objects. Our GaussNav framework demonstrates
    a significant leap in performance, evidenced by an increase in Success weighted
    by Path Length (SPL) from 0.252 to 0.578 on the challenging Habitat-Matterport
    3D (HM3D) dataset.
  project_page: https://xiaohanlei.github.io/projects/GaussNav/
  paper: https://arxiv.org/pdf/2403.11625.pdf
  code: https://github.com/XiaohanLei/GaussNav
  video: null
  tags:
  - Autonomous Driving
  - Code
  - Project
  - Robotics
  thumbnail: assets/thumbnails/lei2024gaussnav.jpg
  publication_date: '2024-03-18T09:56:48+00:00'
- id: herau20243dgscalib
  title: '3DGS-Calib: 3D Gaussian Splatting for Multimodal SpatioTemporal Calibration'
  authors: Quentin Herau, Moussab Bennehar, Arthur Moreau, Nathan Piasco, Luis Roldao,
    Dzmitry Tsishkou, Cyrille Migniot, Pascal Vasseur, Cédric Demonceaux
  year: '2024'
  abstract: Reliable multimodal sensor fusion algorithms re- quire accurate spatiotemporal
    calibration. Recently, targetless calibration techniques based on implicit neural
    representations have proven to provide precise and robust results. Nevertheless,
    such methods are inherently slow to train given the high compu- tational overhead
    caused by the large number of sampled points required for volume rendering. With
    the recent introduction of 3D Gaussian Splatting as a faster alternative to implicit
    representation methods, we propose to leverage this new ren- dering approach to
    achieve faster multi-sensor calibration. We introduce 3DGS-Calib, a new calibration
    method that relies on the speed and rendering accuracy of 3D Gaussian Splatting
    to achieve multimodal spatiotemporal calibration that is accurate, robust, and
    with a substantial speed-up compared to methods relying on implicit neural representations.
    We demonstrate the superiority of our proposal with experimental results on sequences
    from KITTI-360, a widely used driving dataset.
  project_page: https://qherau.github.io/3DGS-Calib/
  paper: https://arxiv.org/pdf/2403.11577
  code: null
  video: https://www.youtube.com/watch?v=9_RE_4xkRcM
  tags:
  - Misc
  - Project
  - Video
  thumbnail: assets/thumbnails/herau20243dgscalib.jpg
  publication_date: '2024-03-18T08:53:03+00:00'
- id: suzuki2024fed3dgs
  title: 'Fed3DGS: Scalable 3D Gaussian Splatting with Federated Learning'
  authors: Teppei Suzuki
  year: '2024'
  abstract: In this work, we present Fed3DGS, a scalable 3D reconstruction framework
    based on 3D Gaussian splatting (3DGS) with federated learning. Existing city-scale
    reconstruction methods typically adopt a centralized approach, which gathers all
    data in a central server and reconstructs scenes. The approach hampers scalability
    because it places a heavy load on the server and demands extensive data storage
    when reconstructing scenes on a scale beyond city-scale. In pursuit of a more
    scalable 3D reconstruction, we propose a federated learning framework with 3DGS,
    which is a decentralized framework and can potentially use distributed computational
    resources across millions of clients. We tailor a distillation-based model update
    scheme for 3DGS and introduce appearance modeling for handling non-IID data in
    the scenario of 3D reconstruction with federated learning. We simulate our method
    on several large-scale benchmarks, and our method demonstrates rendered image
    quality comparable to centralized approaches. In addition, we also simulate our
    method with data collected in different seasons, demonstrating that our framework
    can reflect changes in the scenes and our appearance modeling captures changes
    due to seasonal variations.
  project_page: null
  paper: https://arxiv.org/pdf/2403.11460
  code: https://github.com/DensoITLab/Fed3DGS
  video: null
  tags:
  - Distributed
  - Large-Scale
  thumbnail: assets/thumbnails/suzuki2024fed3dgs.jpg
  publication_date: '2024-03-18T04:26:18+00:00'
- id: xiao2024bridging
  title: Bridging 3D Gaussian and Mesh for Freeview Video Rendering
  authors: Yuting Xiao, Xuan Wang, Jiafei Li, Hongrui Cai, Yanbo Fan, Nan Xue, Minghui
    Yang, Yujun Shen, Shenghua Gao
  year: '2024'
  abstract: This is only a preview version of GauMesh. Recently, primitive-based rendering
    has been proven to achieve convincing results in solving the problem of modeling
    and rendering the 3D dynamic scene from 2D images. Despite this, in the context
    of novel view synthesis, each type of primitive has its inherent defects in terms
    of representation ability. It is difficult to exploit the mesh to depict the fuzzy
    geometry. Meanwhile, the point-based splatting (e.g. the 3D Gaussian Splatting)
    method usually produces artifacts or blurry pixels in the area with smooth geometry
    and sharp textures. As a result, it is difficult, even not impossible, to represent
    the complex and dynamic scene with a single type of primitive. To this end, we
    propose a novel approach, GauMesh, to bridge the 3D Gaussian and Mesh for modeling
    and rendering the dynamic scenes. Given a sequence of tracked mesh as initialization,
    our goal is to simultaneously optimize the mesh geometry, color texture, opacity
    maps, a set of 3D Gaussians, and the deformation field. At a specific time, we
    perform α-blending on the RGB and opacity values based on the merged and re-ordered
    z-buffers from mesh and 3D Gaussian rasterizations. This produces the final rendering,
    which is supervised by the ground-truth image. Experiments demonstrate that our
    approach adapts the appropriate type of primitives to represent the different
    parts of the dynamic scene and outperforms all the baseline methods in both quantitative
    and qualitative comparisons without losing render speed.
  project_page: null
  paper: https://arxiv.org/pdf/2403.11453
  code: null
  video: null
  tags:
  - Avatar
  - Dynamic
  - Meshing
  thumbnail: assets/thumbnails/xiao2024bridging.jpg
  publication_date: '2024-03-18T04:01:26+00:00'
- id: guo2024motionaware
  title: null
  authors: Zhiyang Guo, Wengang Zhou, Li Li, Min Wang, Houqiang Li
  year: '2024'
  abstract: 3D Gaussian Splatting (3DGS) has become an emerging tool for dynamic scene
    reconstruction. However, existing methods focus mainly on extending static 3DGS
    into a time-variant representation, while overlooking the rich motion information
    carried by 2D observations, thus suffering from performance degradation and model
    redundancy. To address the above problem, we propose a novel motion-aware enhancement
    framework for dynamic scene reconstruction, which mines useful motion cues from
    optical flow to improve different paradigms of dynamic 3DGS. Specifically, we
    first establish a correspondence between 3D Gaussian movements and pixel-level
    flow. Then a novel flow augmentation method is introduced with additional insights
    into uncertainty and loss collaboration. Moreover, for the prevalent deformation-based
    paradigm that presents a harder optimization problem, a transient-aware deformation
    auxiliary module is proposed. We conduct extensive experiments on both multi-view
    and monocular scenes to verify the merits of our work. Compared with the baselines,
    our method shows significant superiority in both rendering quality and efficiency.
  project_page: null
  paper: https://arxiv.org/pdf/2403.11447
  code: null
  video: null
  tags:
  - Dynamic
  thumbnail: assets/thumbnails/guo2024motionaware.jpg
  publication_date: '2024-03-18T03:46:26+00:00'
- id: zhang2024bags
  title: 'BAGS: Building Animatable Gaussian Splatting from a Monocular Video with
    Diffusion Priors'
  authors: Tingyang Zhang, Qingzhe Gao, Weiyu Li, Libin Liu, Baoquan Chen
  year: '2024'
  abstract: Animatable 3D reconstruction has significant applications across various
    fields, primarily relying on artists' handcraft creation. Recently, some studies
    have successfully constructed animatable 3D models from monocular videos. However,
    these approaches require sufficient view coverage of the object within the input
    video and typically necessitate significant time and computational costs for training
    and rendering. This limitation restricts the practical applications. In this work,
    we propose a method to build animatable 3D Gaussian Splatting from monocular video
    with diffusion priors. The 3D Gaussian representations significantly accelerate
    the training and rendering process, and the diffusion priors allow the method
    to learn 3D models with limited viewpoints. We also present the rigid regularization
    to enhance the utilization of the priors. We perform an extensive evaluation across
    various real-world videos, demonstrating its superior performance compared to
    the current state-of-the-art methods.
  project_page: null
  paper: https://arxiv.org/pdf/2403.11427
  code: https://github.com/Michaelszj/bags
  video: null
  tags:
  - Code
  - Diffusion
  - Dynamic
  - Monocular
  thumbnail: assets/thumbnails/zhang2024bags.jpg
  publication_date: '2024-03-18T02:44:46+00:00'
- id: liu2024beyond
  title: 'Beyond Uncertainty: Risk-Aware Active View Acquisition for Safe Robot Navigation
    and 3D Scene Understanding with FisherRF'
  authors: Guangyi Liu, Wen Jiang, Boshu Lei, Vivek Pandey, Kostas Daniilidis, Nader
    Motee
  year: '2024'
  abstract: 'This work proposes a novel approach to bolster both the robot''s risk
    assessment and safety measures while deepening its understanding of 3D scenes,
    which is achieved by leveraging Radiance Field (RF) models and 3D Gaussian Splatting.
    To further enhance these capabilities, we incorporate additional sampled views
    from the environment with the RF model. One of our key contributions is the introduction
    of Risk-aware Environment Masking (RaEM), which prioritizes crucial information
    by selecting the next-best-view that maximizes the expected information gain.
    This targeted approach aims to minimize uncertainties surrounding the robot''s
    path and enhance the safety of its navigation. Our method offers a dual benefit:
    improved robot safety and increased efficiency in risk-aware 3D scene reconstruction
    and understanding. Extensive experiments in real-world scenarios demonstrate the
    effectiveness of our proposed approach, highlighting its potential to establish
    a robust and safety-focused framework for active robot exploration and 3D scene
    understanding.'
  project_page: null
  paper: https://arxiv.org/pdf/2403.11396
  code: null
  video: null
  tags:
  - Autonomous Driving
  - Robotics
  - Uncertainty
  thumbnail: assets/thumbnails/liu2024beyond.jpg
  publication_date: '2024-03-18T01:08:18+00:00'
- id: jiang20243dgsreloc
  title: '3DGS-ReLoc: 3D Gaussian Splatting for Map Representation and Visual ReLocalization'
  authors: Peng Jiang, Gaurav Pandey, Srikanth Saripalli
  year: '2024'
  abstract: This paper presents a novel system designed for 3D mapping and visual
    relocalization using 3D Gaussian Splatting. Our proposed method uses LiDAR and
    camera data to create accurate and visually plausible representations of the environment.
    By leveraging LiDAR data to initiate the training of the 3D Gaussian Splatting
    map, our system constructs maps that are both detailed and geometrically accurate.
    To mitigate excessive GPU memory usage and facilitate rapid spatial queries, we
    employ a combination of a 2D voxel map and a KD-tree. This preparation makes our
    method well-suited for visual localization tasks, enabling efficient identification
    of correspondences between the query image and the rendered image from the Gaussian
    Splatting map via normalized cross-correlation (NCC). Additionally, we refine
    the camera pose of the query image using feature-based matching and the Perspective-n-Point
    (PnP) technique. The effectiveness, adaptability, and precision of our system
    are demonstrated through extensive evaluation on the KITTI360 dataset.
  project_page: null
  paper: https://arxiv.org/pdf/2403.11367
  code: null
  video: null
  tags:
  - Poses
  thumbnail: assets/thumbnails/jiang20243dgsreloc.jpg
  publication_date: '2024-03-17T23:06:12+00:00'
- id: tarun2024creating
  title: Creating Seamless 3D Maps Using Radiance Fields
  authors: Sai Tarun Sathyan, Thomas B. Kinsman
  year: '2024'
  abstract: It is desirable to create 3D object models and 3D maps from 2D input images
    for applications such as navigation, virtual tourism, and urban planning. The
    traditional methods of creating 3D maps, (such as photogrammetry), require a large
    number of images and odometry. Additionally, traditional methods have difficulty
    with reflective surfaces and specular reflections; windows and chrome in the scene
    can be problematic. Google Road View is a familiar application, which uses traditional
    methods to fuse a collection of 2D input images into the illusion of a 3D map.
    However, Google Road View does not create an actual 3D object model, only a collection
    of views. The objective of this work is to create an actual 3D object model using
    updated techniques. Neural Radiance Fields (NeRF[1]) has emerged as a potential
    solution, offering the capability to produce more precise and intricate 3D maps.
    Gaussian Splatting[4] is another contemporary technique. This investigation compares
    Neural Radiance Fields to Gaussian Splatting, and describes some of their inner
    workings. Our primary contribution is a method for improving the results of the
    3D reconstructed models. Our results indicate that Gaussian Splatting was superior
    to the NeRF technique.
  project_page: null
  paper: https://arxiv.org/pdf/2403.11364.pdf
  code: null
  video: null
  tags:
  - Misc
  thumbnail: assets/thumbnails/tarun2024creating.jpg
  publication_date: '2024-03-17T22:49:07+00:00'
- id: li2024geogaussian
  title: 'GeoGaussian: Geometry-aware Gaussian Splatting for Scene Rendering'
  authors: Yanyan Li, Chenyu Lyu, Yan Di, Guangyao Zhai, Gim Hee Lee, Federico Tombari
  year: '2024'
  abstract: During the Gaussian Splatting optimization process, the scene's geometry
    can gradually deteriorate if its structure is not deliberately preserved, especially
    in non-textured regions such as walls, ceilings, and furniture surfaces. This
    degradation significantly affects the rendering quality of novel views that deviate
    significantly from the viewpoints in the training data. To mitigate this issue,
    we propose a novel approach called GeoGaussian. Based on the smoothly connected
    areas observed from point clouds, this method introduces a novel pipeline to initialize
    thin Gaussians aligned with the surfaces, where the characteristic can be transferred
    to new generations through a carefully designed densification strategy. Finally,
    the pipeline ensures that the scene's geometry and texture are maintained through
    constrained optimization processes with explicit geometry constraints. Benefiting
    from the proposed architecture, the generative ability of 3D Gaussians is enhanced,
    especially in structured regions. Our proposed pipeline achieves state-of-the-art
    performance in novel view synthesis and geometric reconstruction, as evaluated
    qualitatively and quantitatively on public datasets.
  project_page: https://yanyan-li.github.io/project/gs/geogaussian
  paper: https://arxiv.org/pdf/2403.11324.pdf
  code: https://github.com/yanyan-li/GeoGaussian
  video: null
  tags:
  - Code
  - Densification
  - Project
  - Rendering
  thumbnail: assets/thumbnails/li2024geogaussian.jpg
  publication_date: '2024-03-17T20:06:41+00:00'
- id: jiang2024brightdreamer
  title: 'BrightDreamer: Generic 3D Gaussian Generative Framework for Fast Text-to-3D
    Synthesis'
  authors: Lutao Jiang, Lin Wang
  year: '2024'
  abstract: Text-to-3D synthesis has recently seen intriguing advances by combining
    the text-to-image models with 3D representation methods, e.g., Gaussian Splatting
    (GS), via Score Distillation Sampling (SDS). However, a hurdle of existing methods
    is the low efficiency, per-prompt optimization for a single 3D object. Therefore,
    it is imperative for a paradigm shift from per-prompt optimization to one-stage
    generation for any unseen text prompts, which yet remains challenging. A hurdle
    is how to directly generate a set of millions of 3D Gaussians to represent a 3D
    object. This paper presents BrightDreamer, an end-to-end single-stage approach
    that can achieve generalizable and fast (77 ms) text-to-3D generation. Our key
    idea is to formulate the generation process as estimating the 3D deformation from
    an anchor shape with predefined positions. For this, we first propose a Text-guided
    Shape Deformation (TSD) network to predict the deformed shape and its new positions,
    used as the centers (one attribute) of 3D Gaussians. To estimate the other four
    attributes (i.e., scaling, rotation, opacity, and SH coefficient), we then design
    a novel Text-guided Triplane Generator (TTG) to generate a triplane representation
    for a 3D object. The center of each Gaussian enables us to transform the triplane
    feature into the four attributes. The generated 3D Gaussians can be finally rendered
    at 705 frames per second. Extensive experiments demonstrate the superiority of
    our method over existing methods. Also, BrightDreamer possesses a strong semantic
    understanding capability even for complex text prompts.
  project_page: https://vlislab22.github.io/BrightDreamer/
  paper: https://arxiv.org/pdf/2403.11273
  code: https://github.com/lutao2021/BrightDreamer
  video: https://vlislab22.github.io/BrightDreamer/videos/gui_demo.mp4
  tags:
  - Code
  - Diffusion
  - Project
  - Video
  thumbnail: assets/thumbnails/jiang2024brightdreamer.jpg
  publication_date: '2024-03-17T17:04:45+00:00'
- id: deng2024compact
  title: Compact 3D Gaussian Splatting For Dense Visual SLAM
  authors: Tianchen Deng, Yaohui Chen, Leyan Zhang, Jianfei Yang, Shenghai Yuan, Danwei
    Wang, Weidong Chen
  year: '2024'
  abstract: Recent work has shown that 3D Gaussian-based SLAM enables high-quality
    reconstruction, accurate pose estimation, and real-time rendering of scenes. However,
    these approaches are built on a tremendous number of redundant 3D Gaussian ellipsoids,
    leading to high memory and storage costs, and slow training speed. To address
    the limitation, we propose a compact 3D Gaussian Splatting SLAM system that reduces
    the number and the parameter size of Gaussian ellipsoids. A sliding window-based
    masking strategy is first proposed to reduce the redundant ellipsoids. Then we
    observe that the covariance matrix (geometry) of most 3D Gaussian ellipsoids are
    extremely similar, which motivates a novel geometry codebook to compress 3D Gaussian
    geometric attributes, i.e., the parameters. Robust and accurate pose estimation
    is achieved by a global bundle adjustment method with reprojection loss. Extensive
    experiments demonstrate that our method achieves faster training and rendering
    speed while maintaining the state-of-the-art (SOTA) quality of the scene representation.
  project_page: null
  paper: https://arxiv.org/pdf/2403.11247.pdf
  code: null
  video: null
  tags:
  - SLAM
  thumbnail: assets/thumbnails/deng2024compact.jpg
  publication_date: '2024-03-17T15:41:35+00:00'
- id: wu2024recent
  title: Recent Advances in 3D Gaussian Splatting
  authors: Tong Wu, Yu-Jie Yuan, Ling-Xiao Zhang, Jie Yang, Yan-Pei Cao, Ling-Qi Yan,
    Lin Gao
  year: '2024'
  abstract: The emergence of 3D Gaussian Splatting (3DGS) has greatly accelerated
    the rendering speed of novel view synthesis. Unlike neural implicit representations
    like Neural Radiance Fields (NeRF) that represent a 3D scene with position and
    viewpoint-conditioned neural networks, 3D Gaussian Splatting utilizes a set of
    Gaussian ellipsoids to model the scene so that efficient rendering can be accomplished
    by rasterizing Gaussian ellipsoids into images. Apart from the fast rendering
    speed, the explicit representation of 3D Gaussian Splatting facilitates editing
    tasks like dynamic reconstruction, geometry editing, and physical simulation.
    Considering the rapid change and growing number of works in this field, we present
    a literature review of recent 3D Gaussian Splatting methods, which can be roughly
    classified into 3D reconstruction, 3D editing, and other downstream applications
    by functionality. Traditional point-based rendering methods and the rendering
    formulation of 3D Gaussian Splatting are also illustrated for a better understanding
    of this technique. This survey aims to help beginners get into this field quickly
    and provide experienced researchers with a comprehensive overview, which can stimulate
    the future development of the 3D Gaussian Splatting representation.
  project_page: null
  paper: https://arxiv.org/pdf/2403.11134
  code: null
  video: null
  tags:
  - Review
  thumbnail: assets/thumbnails/wu2024recent.jpg
  publication_date: '2024-03-17T07:57:08+00:00'
- id: liang2024analyticsplatting
  title: 'Analytic-Splatting: Anti-Aliased 3D Gaussian Splatting via Analytic Integration'
  authors: Zhihao Liang, Qi Zhang, Wenbo Hu, Ying Feng, Lei Zhu, Kui Jia
  year: '2024'
  abstract: The 3D Gaussian Splatting (3DGS) gained its popularity recently by combining
    the advantages of both primitive-based and volumetric 3D representations, resulting
    in improved quality and efficiency for 3D scene rendering. However, 3DGS is not
    alias-free, and its rendering at varying resolutions could produce severe blurring
    or jaggies. This is because 3DGS treats each pixel as an isolated, single point
    rather than as an area, causing insensitivity to changes in the footprints of
    pixels. Consequently, this discrete sampling scheme inevitably results in aliasing,
    owing to the restricted sampling bandwidth. In this paper, we derive an analytical
    solution to address this issue. More specifically, we use a conditioned logistic
    function as the analytic approximation of the cumulative distribution function
    (CDF) in a one-dimensional Gaussian signal and calculate the Gaussian integral
    by subtracting the CDFs. We then introduce this approximation in the two-dimensional
    pixel shading, and present Analytic-Splatting, which analytically approximates
    the Gaussian integral within the 2D-pixel window area to better capture the intensity
    response of each pixel. Moreover, we use the approximated response of the pixel
    window integral area to participate in the transmittance calculation of volume
    rendering, making Analytic-Splatting sensitive to the changes in pixel footprint
    at different resolutions. Experiments on various datasets validate that our approach
    has better anti-aliasing capability that gives more details and better fidelity.
  project_page: https://lzhnb.github.io/project-pages/analytic-splatting/
  paper: https://arxiv.org/pdf/2403.11056.pdf
  code: https://github.com/lzhnb/Analytic-Splatting
  video: null
  tags:
  - Antialiasing
  - Code
  - Project
  - Rendering
  thumbnail: assets/thumbnails/liang2024analyticsplatting.jpg
  publication_date: '2024-03-17T02:06:03+00:00'
- id: zhang2024darkgs
  title: 'DarkGS: Learning Neural Illumination and 3D Gaussians Relighting for Robotic
    Exploration in the Dark'
  authors: Tianyi Zhang, Kaining Huang, Weiming Zhi, Matthew Johnson-Roberson
  year: '2024'
  abstract: Humans have the remarkable ability to construct consistent mental models
    of an environment, even under limited or varying levels of illumination. We wish
    to endow robots with this same capability. In this paper, we tackle the challenge
    of constructing a photorealistic scene representation under poorly illuminated
    conditions and with a moving light source. We approach the task of modeling illumination
    as a learning problem, and utilize the developed illumination model to aid in
    scene reconstruction. We introduce an innovative framework that uses a data-driven
    approach, Neural Light Simulators (NeLiS), to model and calibrate the camera-light
    system. Furthermore, we present DarkGS, a method that applies NeLiS to create
    a relightable 3D Gaussian scene model capable of real-time, photorealistic rendering
    from novel viewpoints. We show the applicability and robustness of our proposed
    simulator and system in a variety of real-world environments.
  project_page: null
  paper: https://arxiv.org/pdf/2403.10814
  code: https://github.com/tyz1030/darkgs
  video: https://www.linkedin.com/posts/tianyi-zhang-396b0a186_darkgs-building-3d-gaussians-with-a-torch-activity-7197672371393019905-iY2-?utm_source=share&utm_medium=member_desktop
  tags:
  - Code
  - Misc
  - Robotics
  - Video
  thumbnail: assets/thumbnails/zhang2024darkgs.jpg
  publication_date: '2024-03-16T05:21:42+00:00'
- id: cai2024gspose
  title: 'GS-Pose: Cascaded Framework for Generalizable Segmentation-based 6D Object
    Pose Estimation'
  authors: Dingding Cai, Janne Heikkilä, Esa Rahtu
  year: '2024'
  abstract: This paper introduces GS-Pose, an end-to-end framework for locating and
    estimating the 6D pose of objects. GS-Pose begins with a set of posed RGB images
    of a previously unseen object and builds three distinct representations stored
    in a database. At inference, GS-Pose operates sequentially by locating the object
    in the input image, estimating its initial 6D pose using a retrieval approach,
    and refining the pose with a render-and-compare method. The key insight is the
    application of the appropriate object representation at each stage of the process.
    In particular, for the refinement step, we utilize 3D Gaussian splatting, a novel
    differentiable rendering technique that offers high rendering speed and relatively
    low optimization time. Off-the-shelf toolchains and commodity hardware, such as
    mobile phones, can be used to capture new objects to be added to the database.
    Extensive evaluations on the LINEMOD and OnePose-LowTexture datasets demonstrate
    excellent performance, establishing the new state-of-the-art.
  project_page: https://dingdingcai.github.io/gs-pose/
  paper: https://arxiv.org/pdf/2403.10683
  code: https://github.com/dingdingcai/GSPose
  video: https://youtu.be/SnJazusDLM8
  tags:
  - Poses
  thumbnail: assets/thumbnails/cai2024gspose.jpg
  publication_date: '2024-03-15T21:06:14+00:00'
- id: dahmani2024swag
  title: 'SWAG: Splatting in the Wild images with Appearance-conditioned Gaussians'
  authors: Hiba Dahmani, Moussab Bennehar, Nathan Piasco, Luis Roldao, Dzmitry Tsishkou
  year: '2024'
  abstract: Implicit neural representation methods have shown impressive advancements
    in learning 3D scenes from unstructured in-the-wild photo collections but are
    still limited by the large computational cost of volumetric rendering. More recently,
    3D Gaussian Splatting emerged as a much faster alternative with superior rendering
    quality and training efficiency, especially for small-scale and object-centric
    scenarios. Nevertheless, this technique suffers from poor performance on unstructured
    in-the-wild data. To tackle this, we extend over 3D Gaussian Splatting to handle
    unstructured image collections. We achieve this by modeling appearance to seize
    photometric variations in the rendered images. Additionally, we introduce a new
    mechanism to train transient Gaussians to handle the presence of scene occluders
    in an unsupervised manner. Experiments on diverse photo collection scenes and
    multi-pass acquisition of outdoor landmarks show the effectiveness of our method
    over prior works achieving state-of-the-art results with improved efficiency.
  project_page: null
  paper: https://arxiv.org/pdf/2403.10427.pdf
  code: null
  video: null
  tags:
  - In the Wild
  - Rendering
  thumbnail: assets/thumbnails/dahmani2024swag.jpg
  publication_date: '2024-03-15T16:00:04+00:00'
- id: feng2024fdgaussian
  title: 'FDGaussian: Fast Gaussian Splatting from Single Image via Geometric-aware
    Diffusion Model'
  authors: Qijun Feng, Zhen Xing, Zuxuan Wu, Yu-Gang Jiang
  year: '2024'
  abstract: Reconstructing detailed 3D objects from single-view images remains a challenging
    task due to the limited information available. In this paper, we introduce FDGaussian,
    a novel two-stage framework for single-image 3D reconstruction. Recent methods
    typically utilize pre-trained 2D diffusion models to generate plausible novel
    views from the input image, yet they encounter issues with either multi-view inconsistency
    or lack of geometric fidelity. To overcome these challenges, we propose an orthogonal
    plane decomposition mechanism to extract 3D geometric features from the 2D input,
    enabling the generation of consistent multi-view images. Moreover, we further
    accelerate the state-of-the-art Gaussian Splatting incorporating epipolar attention
    to fuse images from different viewpoints. We demonstrate that FDGaussian generates
    images with high consistency across different views and reconstructs high-quality
    3D objects, both qualitatively and quantitatively.
  project_page: null
  paper: https://arxiv.org/pdf/2403.10242.pdf
  code: null
  video: null
  tags:
  - Diffusion
  thumbnail: assets/thumbnails/feng2024fdgaussian.jpg
  publication_date: '2024-03-15T12:24:36+00:00'
- id: li2024ggrt
  title: 'GGRt: Towards Generalizable 3D Gaussians without Pose Priors in Real-Time'
  authors: Hao Li, Yuanyuan Gao, Dingwen Zhang, Chenming Wu, Yalun Dai, Chen Zhao,
    Haocheng Feng, Errui Ding, Jingdong Wang, Junwei Han
  year: '2024'
  abstract: This paper presents GGRt, a novel approach to generalizable novel view
    synthesis that alleviates the need for real camera poses, complexity in processing
    high-resolution images, and lengthy optimization processes, thus facilitating
    stronger applicability of 3D Gaussian Splatting (3D-GS) in real-world scenarios.
    Specifically, we design a novel joint learning framework that consists of an Iterative
    Pose Optimization Network (IPO-Net) and a Generalizable 3D-Gaussians (G-3DG) model.
    With the joint learning mechanism, the proposed framework can inherently estimate
    robust relative pose information from the image observations and thus primarily
    alleviate the requirement of real camera poses. Moreover, we implement a deferred
    back-propagation mechanism that enables high-resolution training and inference,
    overcoming the resolution constraints of previous methods. To enhance the speed
    and efficiency, we further introduce a progressive Gaussian cache module that
    dynamically adjusts during training and inference. As the first pose-free generalizable
    3D-GS framework, GGRt achieves inference at ≥ 5 FPS and real-time rendering at
    ≥ 100 FPS. Through extensive experimentation, we demonstrate that our method outperforms
    existing NeRF-based pose-free techniques in terms of inference speed and effectiveness.
    It can also approach the real pose-based 3D-GS methods. Our contributions provide
    a significant leap forward for the integration of computer vision and computer
    graphics into practical applications, offering state-of-the-art results on LLFF,
    KITTI, and Waymo Open datasets and enabling real-time rendering for immersive
    experiences.
  project_page: https://3d-aigc.github.io/GGRt/
  paper: https://arxiv.org/pdf/2403.10147
  code: https://github.com/lifuguan/GGRt_official
  video: null
  tags:
  - Code
  - Poses
  - Project
  thumbnail: assets/thumbnails/li2024ggrt.jpg
  publication_date: '2024-03-15T09:47:35+00:00'
- id: xu2024texturegs
  title: 'Texture-GS: Disentangling the Geometry and Texture for 3D Gaussian Splatting
    Editing'
  authors: Tian-Xing Xu, Wenbo Hu, Yu-Kun Lai, Ying Shan, Song-Hai Zhang
  year: '2024'
  abstract: 3D Gaussian splatting, emerging as a groundbreaking approach, has drawn
    increasing attention for its capabilities of high-fidelity reconstruction and
    real-time rendering. However, it couples the appearance and geometry of the scene
    within the Gaussian attributes, which hinders the flexibility of editing operations,
    such as texture swapping. To address this issue, we propose a novel approach,
    namely Texture-GS, to disentangle the appearance from the geometry by representing
    it as a 2D texture mapped onto the 3D surface, thereby facilitating appearance
    editing. Technically, the disentanglement is achieved by our proposed texture
    mapping module, which consists of a UV mapping MLP to learn the UV coordinates
    for the 3D Gaussian centers, a local Taylor expansion of the MLP to efficiently
    approximate the UV coordinates for the ray-Gaussian intersections, and a learnable
    texture to capture the fine-grained appearance. Extensive experiments on the DTU
    dataset demonstrate that our method not only facilitates high-fidelity appearance
    editing but also achieves real-time rendering on consumer-level devices, e.g.
    a single RTX 2080 Ti GPU.
  project_page: https://slothfulxtx.github.io/TexGS/
  paper: https://arxiv.org/pdf/2403.10050
  code: https://github.com/slothfulxtx/Texture-GS
  video: https://www.youtube.com/watch?v=mn_vi4a_fu4
  tags:
  - Code
  - Editing
  - Project
  - Texturing
  - Video
  thumbnail: assets/thumbnails/xu2024texturegs.jpg
  publication_date: '2024-03-15T06:42:55+00:00'
- id: li2024controllable
  title: Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting
  authors: Zhiqi Li, Yiming Chen, Lingzhe Zhao, Peidong Liu
  year: '2024'
  abstract: While text-to-3D and image-to-3D generation tasks have received considerable
    attention, one important but under-explored field between them is controllable
    text-to-3D generation, which we mainly focus on in this work. To address this
    task, 1) we introduce ControlNet (MVControl), a novel neural network architecture
    designed to enhance existing pre-trained multi-view diffusion models by integrating
    additional input conditions, such as edge, depth, normal, and scribble maps. Our
    innovation lies in the introduction of a conditioning module that controls the
    base diffusion model using both local and global embeddings, which are computed
    from the input condition images and camera poses. Once trained, MVControl is able
    to offer 3D diffusion guidance for optimization-based 3D generation. And, 2) we
    propose an efficient multi-stage 3D generation pipeline that leverages the benefits
    of recent large reconstruction models and score distillation algorithm. Building
    upon our MVControl architecture, we employ a unique hybrid diffusion guidance
    method to direct the optimization process. In pursuit of efficiency, we adopt
    3D Gaussians as our representation instead of the commonly used implicit representations.
    We also pioneer the use of SuGaR, a hybrid representation that binds Gaussians
    to mesh triangle faces. This approach alleviates the issue of poor geometry in
    3D Gaussians and enables the direct sculpting of fine-grained geometry on the
    mesh. Extensive experiments demonstrate that our method achieves robust generalization
    and enables the controllable generation of high-quality 3D content.
  project_page: https://lizhiqi49.github.io/MVControl/
  paper: https://arxiv.org/pdf/2403.09981.pdf
  code: https://github.com/WU-CVGL/MVControl-threestudio
  video: null
  tags:
  - Code
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/li2024controllable.jpg
  publication_date: '2024-03-15T02:57:20+00:00'
  date_source: arxiv
- id: yu2024densoft
  title: 'Den-SOFT: Dense Space-Oriented Light Field DataseT for 6-DOF Immersive Experience'
  authors: Xiaohang Yu, Zhengxian Yang, Shi Pan, Yuqi Han, Haoxiang Wang, Jun Zhang,
    Shi Yan, Borong Lin, Lei Yang, Tao Yu, Lu Fang
  year: '2024'
  abstract: We have built a custom mobile multi-camera large-space dense light field
    capture system, which provides a series of high-quality and sufficiently dense
    light field images for various scenarios. Our aim is to contribute to the development
    of popular 3D scene reconstruction algorithms such as IBRnet, NeRF, and 3D Gaussian
    splitting. More importantly, the collected dataset, which is much denser than
    existing datasets, may also inspire space-oriented light field reconstruction,
    which is potentially different from object-centric 3D reconstruction, for immersive
    VR/AR experiences. We utilized a total of 40 GoPro 10 cameras, capturing images
    of 5k resolution. The number of photos captured for each scene is no less than
    1000, and the average density (view number within a unit sphere) is 134.68. It
    is also worth noting that our system is capable of efficiently capturing large
    outdoor scenes. Addressing the current lack of large-space and dense light field
    datasets, we made efforts to include elements such as sky, reflections, lights
    and shadows that are of interest to researchers in the field of 3D reconstruction
    during the data capture process. Finally, we validated the effectiveness of our
    provided dataset on three popular algorithms and also integrated the reconstructed
    3DGS results into the Unity engine, demonstrating the potential of utilizing our
    datasets to enhance the realism of virtual reality (VR) and create feasible interactive
    spaces.
  project_page: https://metaverse-ai-lab-thu.github.io/Den-SOFT/
  paper: https://arxiv.org/pdf/2403.09973.pdf
  code: null
  video: null
  tags:
  - Project
  - Virtual Reality
  thumbnail: assets/thumbnails/yu2024densoft.jpg
  publication_date: '2024-03-15T02:39:44+00:00'
- id: swann2024touchgs
  title: 'Touch-GS: Visual-Tactile Supervised 3D Gaussian Splatting'
  authors: Aiden Swann, Matthew Strong, Won Kyung Do, Gadiel Sznaier Camps, Mac Schwager,
    Monroe Kennedy III
  year: '2024'
  abstract: In this work, we propose a novel method to supervise 3D Gaussian Splatting
    (3DGS) scenes using optical tactile sensors. Optical tactile sensors have become
    widespread in their use in robotics for manipulation and object representation;
    however, raw optical tactile sensor data is unsuitable to directly supervise a
    3DGS scene. Our representation leverages a Gaussian Process Implicit Surface to
    implicitly represent the object, combining many touches into a unified representation
    with uncertainty. We merge this model with a monocular depth estimation network,
    which is aligned in a two stage process, coarsely aligning with a depth camera
    and then finely adjusting to match our touch data. For every training image, our
    method produces a corresponding fused depth and uncertainty map. Utilizing this
    additional information, we propose a new loss function, variance weighted depth
    supervised loss, for training the 3DGS scene model. We leverage the DenseTact
    optical tactile sensor and RealSense RGB-D camera to show that combining touch
    and vision in this manner leads to quantitatively and qualitatively better results
    than vision or touch alone in a few-view scene syntheses on opaque as well as
    on reflective and transparent objects.
  project_page: https://armlabstanford.github.io/touch-gs
  paper: https://arxiv.org/pdf/2403.09875.pdf
  code: https://github.com/armlabstanford/Touch-GS
  video: https://www.youtube.com/watch?v=FqejaTEt7aU
  tags:
  - Code
  - Project
  - Rendering
  - Robotics
  - Sparse
  - Video
  thumbnail: assets/thumbnails/swann2024touchgs.jpg
  publication_date: '2024-03-14T21:09:59+00:00'
- id: zheng2024gaussiangrasper
  title: 'GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic
    Grasping'
  authors: Yuhang Zheng, Xiangyu Chen, Yupeng Zheng, Songen Gu, Runyi Yang, Bu Jin,
    Pengfei Li, Chengliang Zhong, Zengmao Wang, Lina Liu, Chao Yang, Dawei Wang, Zhen
    Chen, Xiaoxiao Long, Meiqing Wang
  year: '2024'
  abstract: Constructing a 3D scene capable of accommodating open-ended language queries,
    is a pivotal pursuit, particularly within the domain of robotics. Such technology
    facilitates robots in executing object manipulations based on human language directives.
    To tackle this challenge, some research efforts have been dedicated to the development
    of language-embedded implicit fields. However, implicit fields (e.g. NeRF) encounter
    limitations due to the necessity of processing a large number of input views for
    reconstruction, coupled with their inherent inefficiencies in inference. Thus,
    we present the GaussianGrasper, which utilizes 3D Gaussian Splatting to explicitly
    represent the scene as a collection of Gaussian primitives. Our approach takes
    a limited set of RGB-D views and employs a tile-based splatting technique to create
    a feature field. In particular, we propose an Efficient Feature Distillation (EFD)
    module that employs contrastive learning to efficiently and accurately distill
    language embeddings derived from foundational models. With the reconstructed geometry
    of the Gaussian field, our method enables the pre-trained grasping model to generate
    collision-free grasp pose candidates. Furthermore, we propose a normal-guided
    grasp module to select the best grasp pose. Through comprehensive real-world experiments,
    we demonstrate that GaussianGrasper enables robots to accurately query and grasp
    objects with language instructions, providing a new solution for language-guided
    manipulation tasks.
  project_page: https://mrsecant.github.io/GaussianGrasper/
  paper: https://arxiv.org/pdf/2403.09637
  code: https://github.com/MrSecant/GaussianGrasper
  video: null
  tags:
  - Code
  - Language Embedding
  - Project
  - Robotics
  - Segmentation
  thumbnail: assets/thumbnails/zheng2024gaussiangrasper.jpg
  publication_date: '2024-03-14T17:59:46+00:00'
- id: zhong2024reconstruction
  title: Reconstruction and Simulation of Elastic Objects with Spring-Mass 3D Gaussians
  authors: Licheng Zhong, Hong-Xing Yu, Jiajun Wu, Yunzhu Li
  year: '2024'
  abstract: Reconstructing and simulating elastic objects from visual observations
    is crucial for applications in computer vision and robotics. Existing methods,
    such as 3D Gaussians, provide modeling for 3D appearance and geometry but lack
    the ability to simulate physical properties or optimize parameters for heterogeneous
    objects. We propose Spring-Gaus, a novel framework that integrates 3D Gaussians
    with physics-based simulation for reconstructing and simulating elastic objects
    from multi-view videos. Our method utilizes a 3D Spring-Mass model, enabling the
    optimization of physical parameters at the individual point level while decoupling
    the learning of physics and appearance. This approach achieves great sample efficiency,
    enhances generalization, and reduces sensitivity to the distribution of simulation
    particles. We evaluate Spring-Gaus on both synthetic and real-world datasets,
    demonstrating accurate reconstruction and simulation of elastic objects. This
    includes future prediction and simulation under varying initial states and environmental
    parameters.
  project_page: https://zlicheng.com/spring_gaus/
  paper: https://arxiv.org/pdf/2403.09434
  code: https://github.com/Colmar-zlicheng/Spring-Gaus
  video: null
  tags:
  - Code
  - Dynamic
  - Physics
  - Project
  thumbnail: assets/thumbnails/zhong2024reconstruction.jpg
  publication_date: '2024-03-14T14:25:10+00:00'
- id: jung2024raings
  title: 'RAIN-GS: Relaxing Accurate Initialization Constraint for 3D Gaussian Splatting'
  authors: Jaewoo Jung, Jisang Han, Honggyu An, Jiwon Kang, Seonghoon Park, Seungryong
    Kim
  year: '2024'
  abstract: 3D Gaussian splatting (3DGS) has recently demonstrated impressive capabilities
    in real-time novel view synthesis and 3D reconstruction. However, 3DGS heavily
    depends on the accurate initialization derived from Structure-from-Motion (SfM)
    methods. When trained with randomly initialized point clouds, 3DGS often fails
    to maintain its ability to produce high-quality images, undergoing large performance
    drops of 4-5 dB in PSNR in general. Through extensive analysis of SfM initialization
    in the frequency domain and analysis of a 1D regression task with multiple 1D
    Gaussians, we propose a novel optimization strategy dubbed RAIN-GS (Relaxing Accurate
    INitialization Constraint for 3D Gaussian Splatting) that successfully trains
    3D Gaussians from randomly initialized point clouds. We show the effectiveness
    of our strategy through quantitative and qualitative comparisons on standard datasets,
    largely improving the performance in all settings.
  project_page: https://ku-cvlab.github.io/RAIN-GS/
  paper: https://arxiv.org/pdf/2403.09413
  code: https://github.com/cvlab-kaist/RAIN-GS
  video: null
  tags:
  - Code
  - Densification
  - Project
  - Rendering
  thumbnail: assets/thumbnails/jung2024raings.jpg
  publication_date: '2024-03-14T14:04:21+00:00'
- id: di2024hyper3dgtextto3d
  title: Hyper-3DG:Text-to-3D Gaussian Generation via Hypergraph
  authors: Donglin Di, Jiahui Yang, Chaofan Luo, Zhou Xue, Wei Chen, Xun Yang, Yue
    Gao
  year: '2024'
  abstract: Text-to-3D generation represents an exciting field that has seen rapid
    advancements, facilitating the transformation of textual descriptions into detailed
    3D models. However, current progress often neglects the intricate high-order correlation
    of geometry and texture within 3D objects, leading to challenges such as over-smoothness,
    over-saturation and the Janus problem. In this work, we propose a method named
    ``3D Gaussian Generation via Hypergraph (Hyper-3DG)'', designed to capture the
    sophisticated high-order correlations present within 3D objects. Our framework
    is anchored by a well-established mainflow and an essential module, named ``Geometry
    and Texture Hypergraph Refiner (HGRefiner)''. This module not only refines the
    representation of 3D Gaussians but also accelerates the update process of these
    3D Gaussians by conducting the Patch-3DGS Hypergraph Learning on both explicit
    attributes and latent visual features. Our framework allows for the production
    of finely generated 3D objects within a cohesive optimization, effectively circumventing
    degradation. Extensive experimentation has shown that our proposed method significantly
    enhances the quality of 3D generation while incurring no additional computational
    overhead for the underlying framework.
  project_page: null
  paper: https://arxiv.org/pdf/2403.09236.pdf
  code: null
  video: null
  tags:
  - Diffusion
  thumbnail: assets/thumbnails/di2024hyper3dgtextto3d.jpg
  publication_date: '2024-03-14T09:59:55+00:00'
- id: feng2024a
  title: A New Split Algorithm for 3D Gaussian Splatting
  authors: Qiyuan Feng, Gengchen Cao, Haoxiang Chen, Tai-Jiang Mu, Ralph R. Martin,
    Shi-Min Hu
  year: '2024'
  abstract: 3D Gaussian splatting models, as a novel explicit 3D representation, have
    been applied in many domains recently, such as explicit geometric editing and
    geometry generation. Progress has been rapid. However, due to their mixed scales
    and cluttered shapes, 3D Gaussian splatting models can produce a blurred or needle-like
    effect near the surface. At the same time, 3D Gaussian splatting models tend to
    flatten large untextured regions, yielding a very sparse point cloud. These problems
    are caused by the non-uniform nature of 3D Gaussian splatting models, so in this
    paper, we propose a new 3D Gaussian splitting algorithm, which can produce a more
    uniform and surface-bounded 3D Gaussian splatting model. Our algorithm splits
    an N-dimensional Gaussian into two N-dimensional Gaussians. It ensures consistency
    of mathematical characteristics and similarity of appearance, allowing resulting
    3D Gaussian splatting models to be more uniform and a better fit to the underlying
    surface, and thus more suitable for explicit editing, point cloud extraction and
    other tasks. Meanwhile, our 3D Gaussian splitting approach has a very simple closed-form
    solution, making it readily applicable to any 3D Gaussian model.
  project_page: null
  paper: https://arxiv.org/pdf/2403.09143
  code: null
  video: null
  tags:
  - Densification
  thumbnail: assets/thumbnails/feng2024a.jpg
  publication_date: '2024-03-14T07:42:12+00:00'
- id: wu2024gaussctrl
  title: 'GaussCtrl: Multi-View Consistent Text-Driven 3D Gaussian Splatting Editing'
  authors: Jing Wu, Jia-Wang Bian, Xinghui Li, Guangrun Wang, Ian Reid, Philip Torr,
    Victor Adrian Prisacariu
  year: '2024'
  abstract: 'We propose GaussCtrl, a text-driven method to edit a 3D scene reconstructed
    by the 3D Gaussian Splatting (3DGS). Our method first renders a collection of
    images by using the 3DGS and edits them by using a pre-trained 2D diffusion model
    (ControlNet) based on the input prompt, which is then used to optimise the 3D
    model. Our key contribution is multi-view consistent editing, which enables editing
    all images together instead of iteratively editing one image while updating the
    3D model as in previous works. It leads to faster editing as well as higher visual
    quality. This is achieved by the two terms: (a) depth-conditioned editing that
    enforces geometric consistency across multi-view images by leveraging naturally
    consistent depth maps. (b) attention-based latent code alignment that unifies
    the appearance of edited images by conditioning their editing to several reference
    views through self and cross-view attention between images'' latent representations.
    Experiments demonstrate that our method achieves faster editing and better visual
    results than previous state-of-the-art methods.'
  project_page: https://gaussctrl.active.vision/
  paper: https://arxiv.org/pdf/2403.08733.pdf
  code: https://github.com/ActiveVisionLab/gaussctrl
  video: https://gaussctrl.active.vision/assets/teaser_vid.mp4
  tags:
  - Code
  - Editing
  - Project
  - Video
  thumbnail: assets/thumbnails/wu2024gaussctrl.jpg
  publication_date: '2024-03-13T17:35:28+00:00'
- id: zhang2024gaussianimage
  title: 'GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian
    Splatting'
  authors: Xinjie Zhang, Xingtong Ge, Tongda Xu, Dailan He, Yan Wang, Hongwei Qin,
    Guo Lu, Jing Geng, Jun Zhang
  year: '2024'
  abstract: Implicit neural representations (INRs) recently achieved great success
    in image representation and compression, offering high visual quality and fast
    rendering speeds with 10-1000 FPS, assuming sufficient GPU resources are available.
    However, this requirement often hinders their use on low-end devices with limited
    memory. In response, we propose a groundbreaking paradigm of image representation
    and compression by 2D Gaussian Splatting, named GaussianImage. We first introduce
    2D Gaussian to represent the image, where each Gaussian has 8 parameters including
    position, covariance and color. Subsequently, we unveil a novel rendering algorithm
    based on accumulated summation. Remarkably, our method with a minimum of 3× lower
    GPU memory usage and 5× faster fitting time not only rivals INRs (e.g., WIRE,
    I-NGP) in representation performance, but also delivers a faster rendering speed
    of 1500-2000 FPS regardless of parameter size. Furthermore, we integrate existing
    vector quantization technique to build an image codec. Experimental results demonstrate
    that our codec attains rate-distortion performance comparable to compression-based
    INRs such as COIN and COIN++, while facilitating decoding speeds of approximately
    1000 FPS. Additionally, preliminary proof of concept shows that our codec surpasses
    COIN and COIN++ in performance when using partial bits-back coding.
  project_page: https://xingtongge.github.io/GaussianImage-page/
  paper: https://arxiv.org/pdf/2403.08551
  code: https://github.com/Xinjie-Q/GaussianImage
  video: null
  tags:
  - 2DGS
  - Code
  - Compression
  - Project
  thumbnail: assets/thumbnails/zhang2024gaussianimage.jpg
  publication_date: '2024-03-13T14:02:54+00:00'
- id: saroha2024gaussian
  title: Gaussian Splatting in Style
  authors: Abhishek Saroha, Mariia Gladkova, Cecilia Curreli, Tarun Yenamandra, Daniel
    Cremers
  year: '2024'
  abstract: Scene stylization extends the work of neural style transfer to three spatial
    dimensions. A vital challenge in this problem is to maintain the uniformity of
    the stylized appearance across a multi-view setting. A vast majority of the previous
    works achieve this by optimizing the scene with a specific style image. In contrast,
    we propose a novel architecture trained on a collection of style images, that
    at test time produces high quality stylized novel views. Our work builds up on
    the framework of 3D Gaussian splatting. For a given scene, we take the pretrained
    Gaussians and process them using a multi resolution hash grid and a tiny MLP to
    obtain the conditional stylised views. The explicit nature of 3D Gaussians give
    us inherent advantages over NeRF-based methods including geometric consistency,
    along with having a fast training and rendering regime. This enables our method
    to be useful for vast practical use cases such as in augmented or virtual reality
    applications. Through our experiments, we show our methods achieve state-of-the-art
    performance with superior visual quality on various indoor and outdoor real-world
    data.
  project_page: null
  paper: https://arxiv.org/pdf/2403.08498
  code: null
  video: null
  tags:
  - Style Transfer
  thumbnail: assets/thumbnails/saroha2024gaussian.jpg
  publication_date: '2024-03-13T13:06:31+00:00'
- id: lu2024manigaussian
  title: 'ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation'
  authors: Guanxing Lu, Shiyi Zhang, Ziwei Wang, Changliu Liu, Jiwen Lu, Yansong Tang
  year: '2024'
  abstract: Performing language-conditioned robotic manipulation tasks in unstructured
    environments is highly demanded for general intelligent robots. Conventional robotic
    manipulation methods usually learn semantic representation of the observation
    for action prediction, which ignores the scene-level spatiotemporal dynamics for
    human goal completion. In this paper, we propose a dynamic Gaussian Splatting
    method named ManiGaussian for multi-task robotic manipulation, which mines scene
    dynamics via future scene reconstruction. Specifically, we first formulate the
    dynamic Gaussian Splatting framework that infers the semantics propagation in
    the Gaussian embedding space, where the semantic representation is leveraged to
    predict the optimal robot action. Then, we build a Gaussian world model to parameterize
    the distribution in our dynamic Gaussian Splatting framework, which provides informative
    supervision in the interactive environment via future scene reconstruction. We
    evaluate our ManiGaussian on 10 RLBench tasks with 166 variations, and the results
    demonstrate our framework can outperform the state-of-the-art methods by 13.1%
    in average success rate.
  project_page: https://guanxinglu.github.io/ManiGaussian/
  paper: https://arxiv.org/pdf/2403.08321.pdf
  code: https://github.com/GuanxingLu/ManiGaussian
  video: null
  tags:
  - Code
  - Dynamic
  - Project
  - Robotics
  thumbnail: assets/thumbnails/lu2024manigaussian.jpg
  publication_date: '2024-03-13T08:06:41+00:00'
- id: liu2024stylegaussian
  title: 'StyleGaussian: Instant 3D Style Transfer with Gaussian Splatting'
  authors: Kunhao Liu, Fangneng Zhan, Muyu Xu, Christian Theobalt, Ling Shao, Shijian
    Lu
  year: '2024'
  abstract: 'We introduce StyleGaussian, a novel 3D style transfer technique that
    allows instant transfer of any image''s style to a 3D scene at 10 frames per second
    (fps). Leveraging 3D Gaussian Splatting (3DGS), StyleGaussian achieves style transfer
    without compromising its real-time rendering ability and multi-view consistency.
    It achieves instant style transfer with three steps: embedding, transfer, and
    decoding. Initially, 2D VGG scene features are embedded into reconstructed 3D
    Gaussians. Next, the embedded features are transformed according to a reference
    style image. Finally, the transformed features are decoded into the stylized RGB.
    StyleGaussian has two novel designs. The first is an efficient feature rendering
    strategy that first renders low-dimensional features and then maps them into high-dimensional
    features while embedding VGG features. It cuts the memory consumption significantly
    and enables 3DGS to render the high-dimensional memory-intensive features. The
    second is a K-nearest-neighbor-based 3D CNN. Working as the decoder for the stylized
    features, it eliminates the 2D CNN operations that compromise strict multi-view
    consistency. Extensive experiments show that StyleGaussian achieves instant 3D
    stylization with superior stylization quality while preserving real-time rendering
    and strict multi-view consistency.'
  project_page: https://kunhao-liu.github.io/StyleGaussian/
  paper: https://arxiv.org/pdf/2403.07807.pdf
  code: https://github.com/Kunhao-Liu/StyleGaussian
  video: null
  tags:
  - Code
  - Project
  - Style Transfer
  thumbnail: assets/thumbnails/liu2024stylegaussian.jpg
  publication_date: '2024-03-12T16:44:52+00:00'
- id: zhu2024semgaussslam
  title: 'SemGauss-SLAM: Dense Semantic Gaussian Splatting SLAM'
  authors: Siting Zhu, Renjie Qin, Guangming Wang, Jiuming Liu, Hesheng Wang
  year: '2024'
  abstract: We propose SemGauss-SLAM, the first semantic SLAM system utilizing 3D
    Gaussian representation, that enables accurate 3D semantic mapping, robust camera
    tracking, and high-quality rendering in real-time. In this system, we incorporate
    semantic feature embedding into 3D Gaussian representation, which effectively
    encodes semantic information within the spatial layout of the environment for
    precise semantic scene representation. Furthermore, we propose feature-level loss
    for updating 3D Gaussian representation, enabling higher-level guidance for 3D
    Gaussian optimization. In addition, to reduce cumulative drift and improve reconstruction
    accuracy, we introduce semantic-informed bundle adjustment leveraging semantic
    associations for joint optimization of 3D Gaussian representation and camera poses,
    leading to more robust tracking and consistent mapping. Our SemGauss-SLAM method
    demonstrates superior performance over existing dense semantic SLAM methods in
    terms of mapping and tracking accuracy on Replica and ScanNet datasets, while
    also showing excellent capabilities in novel-view semantic synthesis and 3D semantic
    mapping.
  project_page: null
  paper: https://arxiv.org/pdf/2403.07494.pdf
  code: null
  video: null
  tags:
  - SLAM
  thumbnail: assets/thumbnails/zhu2024semgaussslam.jpg
  publication_date: '2024-03-12T10:33:26+00:00'
- id: li2024dngaussian
  title: 'DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local
    Depth Normalization'
  authors: Jiahe Li, Jiawei Zhang, Xiao Bai, Jin Zheng, Xin Ning, Jun Zhou, Lin Gu
  year: '2024'
  abstract: Radiance fields have demonstrated impressive performance in synthesizing
    novel views from sparse input views, yet prevailing methods suffer from high training
    costs and slow inference speed. This paper introduces DNGaussian, a depth-regularized
    framework based on 3D Gaussian radiance fields, offering real-time and high-quality
    few-shot novel view synthesis at low costs. Our motivation stems from the highly
    efficient representation and surprising quality of the recent 3D Gaussian Splatting,
    despite it will encounter a geometry degradation when input views decrease. In
    the Gaussian radiance fields, we find this degradation in scene geometry primarily
    lined to the positioning of Gaussian primitives and can be mitigated by depth
    constraint. Consequently, we propose a Hard and Soft Depth Regularization to restore
    accurate scene geometry under coarse monocular depth supervision while maintaining
    a fine-grained color appearance. To further refine detailed geometry reshaping,
    we introduce Global-Local Depth Normalization, enhancing the focus on small local
    depth changes. Extensive experiments on LLFF, DTU, and Blender datasets demonstrate
    that DNGaussian outperforms state-of-the-art methods, achieving comparable or
    better results with significantly reduced memory cost, a 25× reduction in training
    time, and over 3000× faster rendering speed.
  project_page: https://fictionarry.github.io/DNGaussian/
  paper: https://arxiv.org/pdf/2403.06912.pdf
  code: https://github.com/Fictionarry/DNGaussian
  video: https://www.youtube.com/watch?v=WKXCFNJHZ4o
  tags:
  - Code
  - Project
  - Sparse
  - Video
  thumbnail: assets/thumbnails/li2024dngaussian.jpg
  publication_date: '2024-03-11T17:02:11+00:00'
- id: zhang2024fregs
  title: 'FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization'
  authors: Jiahui Zhang, Fangneng Zhan, Muyu Xu, Shijian Lu, Eric Xing
  year: '2024'
  abstract: 3D Gaussian splatting has achieved very impressive performance in real-time
    novel view synthesis. However, it often suffers from over-reconstruction during
    Gaussian densification where high-variance image regions are covered by a few
    large Gaussians only, leading to blur and artifacts in the rendered images. We
    design a progressive frequency regularization (FreGS) technique to tackle the
    over-reconstruction issue within the frequency space. Specifically, FreGS performs
    coarse-to-fine Gaussian densification by exploiting low-to-high frequency components
    that can be easily extracted with low-pass and high-pass filters in the Fourier
    space. By minimizing the discrepancy between the frequency spectrum of the rendered
    image and the corresponding ground truth, it achieves high-quality Gaussian densification
    and alleviates the over-reconstruction of Gaussian splatting effectively. Experiments
    over multiple widely adopted benchmarks (e.g., Mip-NeRF360, Tanks-and-Temples
    and Deep Blending) show that FreGS achieves superior novel view synthesis and
    outperforms the state-of-the-art consistently.
  project_page: null
  paper: https://arxiv.org/pdf/2403.06908.pdf
  code: null
  video: null
  tags:
  - Densification
  thumbnail: assets/thumbnails/zhang2024fregs.jpg
  publication_date: '2024-03-11T17:00:27+00:00'
- id: palandra2024gsedit
  title: 'GSEdit: Efficient Text-Guided Editing of 3D Objects via Gaussian Splatting'
  authors: Francesco Palandra, Andrea Sanchietti, Daniele Baieri, Emanuele Rodolà
  year: '2024'
  abstract: We present GSEdit, a pipeline for text-guided 3D object editing based
    on Gaussian Splatting models. Our method enables the editing of the style and
    appearance of 3D objects without altering their main details, all in a matter
    of minutes on consumer hardware. We tackle the problem by leveraging Gaussian
    splatting to represent 3D scenes, and we optimize the model while progressively
    varying the image supervision by means of a pretrained image-based diffusion model.
    The input object may be given as a 3D triangular mesh, or directly provided as
    Gaussians from a generative model such as DreamGaussian. GSEdit ensures consistency
    across different viewpoints, maintaining the integrity of the original object's
    information. Compared to previously proposed methods relying on NeRF-like MLP
    models, GSEdit stands out for its efficiency, making 3D editing tasks much faster.
    Our editing process is refined via the application of the SDS loss, ensuring that
    our edits are both precise and accurate. Our comprehensive evaluation demonstrates
    that GSEdit effectively alters object shape and appearance following the given
    textual instructions while preserving their coherence and detail.
  project_page: null
  paper: https://arxiv.org/pdf/2403.05154.pdf
  code: null
  video: null
  tags:
  - Editing
  thumbnail: assets/thumbnails/palandra2024gsedit.jpg
  publication_date: '2024-03-08T08:42:23+00:00'
- id: shao2024splattingavatar
  title: 'SplattingAvatar: Realistic Real-Time Human Avatars with Mesh-Embedded Gaussian
    Splatting'
  authors: Zhijing Shao, Zhaolong Wang, Zhuang Li, Duotun Wang, Xiangru Lin, Yu Zhang,
    Mingming Fan, Zeyu Wang
  year: '2024'
  abstract: We present SplattingAvatar, a hybrid 3D representation of photorealistic
    human avatars with Gaussian Splatting embedded on a triangle mesh, which renders
    over 300 FPS on a modern GPU and 30 FPS on a mobile device. We disentangle the
    motion and appearance of a virtual human with explicit mesh geometry and implicit
    appearance modeling with Gaussian Splatting. The Gaussians are defined by barycentric
    coordinates and displacement on a triangle mesh as Phong surfaces. We extend lifted
    optimization to simultaneously optimize the parameters of the Gaussians while
    walking on the triangle mesh. SplattingAvatar is a hybrid representation of virtual
    humans where the mesh represents low-frequency motion and surface deformation,
    while the Gaussians take over the high-frequency geometry and detailed appearance.
    Unlike existing deformation methods that rely on an MLP-based linear blend skinning
    (LBS) field for motion, we control the rotation and translation of the Gaussians
    directly by mesh, which empowers its compatibility with various animation techniques,
    e.g., skeletal animation, blend shapes, and mesh editing. Trainable from monocular
    videos for both full-body and head avatars, SplattingAvatar shows state-of-the-art
    rendering quality across multiple datasets.
  project_page: https://initialneil.github.io/SplattingAvatar
  paper: https://arxiv.org/pdf/2403.05087.pdf
  code: https://github.com/initialneil/SplattingAvatar
  video: https://www.youtube.com/watch?v=IzC-fLvdntA
  tags:
  - Avatar
  - Code
  - Project
  - Video
  thumbnail: assets/thumbnails/shao2024splattingavatar.jpg
  publication_date: '2024-03-08T06:28:09+00:00'
- id: peng2024bags
  title: 'BAGS: Blur Agnostic Gaussian Splatting through Multi-Scale Kernel Modeling'
  authors: Cheng Peng, Yutao Tang, Yifan Zhou, Nengyu Wang, Xijun Liu, Deming Li,
    Rama Chellappa
  year: '2024'
  abstract: Recent efforts in using 3D Gaussians for scene reconstruction and novel
    view synthesis can achieve impressive results on curated benchmarks; however,
    images captured in real life are often blurry. In this work, we analyze the robustness
    of Gaussian-Splatting-based methods against various image blur, such as motion
    blur, defocus blur, downscaling blur, \etc. Under these degradations, Gaussian-Splatting-based
    methods tend to overfit and produce worse results than Neural-Radiance-Field-based
    methods. To address this issue, we propose Blur Agnostic Gaussian Splatting (BAGS).
    BAGS introduces additional 2D modeling capacities such that a 3D-consistent and
    high quality scene can be reconstructed despite image-wise blur. Specifically,
    we model blur by estimating per-pixel convolution kernels from a Blur Proposal
    Network (BPN). BPN is designed to consider spatial, color, and depth variations
    of the scene to maximize modeling capacity. Additionally, BPN also proposes a
    quality-assessing mask, which indicates regions where blur occur. Finally, we
    introduce a coarse-to-fine kernel optimization scheme; this optimization scheme
    is fast and avoids sub-optimal solutions due to a sparse point cloud initialization,
    which often occurs when we apply Structure-from-Motion on blurry images. We demonstrate
    that BAGS achieves photorealistic renderings under various challenging blur conditions
    and imaging geometry, while significantly improving upon existing approaches.
  project_page: https://nwang43jhu.github.io/BAGS/
  paper: https://arxiv.org/pdf/2403.04926.pdf
  code: https://github.com/snldmt/BAGS
  video: null
  tags:
  - Code
  - Deblurring
  - Project
  thumbnail: assets/thumbnails/peng2024bags.jpg
  publication_date: '2024-03-07T22:21:08+00:00'
- id: cai2024radiative
  title: Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis
  authors: TYuanhao Cai, Yixun Liang, Jiahao Wang, Angtian Wang, Yulun Zhang, Xiaokang
    Yang, Zongwei Zhou, Alan Yuille
  year: '2024'
  abstract: X-ray is widely applied for transmission imaging due to its stronger penetration
    than natural light. When rendering novel view X-ray projections, existing methods
    mainly based on NeRF suffer from long training time and slow inference speed.
    In this paper, we propose a 3D Gaussian splatting-based framework, namely X-Gaussian,
    for X-ray novel view synthesis. Firstly, we redesign a radiative Gaussian point
    cloud model inspired by the isotropic nature of X-ray imaging. Our model excludes
    the influence of view direction when learning to predict the radiation intensity
    of 3D points. Based on this model, we develop a Differentiable Radiative Rasterization
    (DRR) with CUDA implementation. Secondly, we customize an Angle-pose Cuboid Uniform
    Initialization (ACUI) strategy that directly uses the parameters of the X-ray
    scanner to compute the camera information and then uniformly samples point positions
    within a cuboid enclosing the scanned object. Experiments show that our X-Gaussian
    outperforms state-of-the-art methods by 6.5 dB while enjoying less than 15% training
    time and over 73x inference speed. The application on sparse-view CT reconstruction
    also reveals the practical values of our method.
  project_page: null
  paper: https://arxiv.org/pdf/2403.04116.pdf
  code: null
  video: null
  tags:
  - Medicine
  thumbnail: assets/thumbnails/cai2024radiative.jpg
  publication_date: '2024-03-07T00:12:08+00:00'
- id: chen2024splatnav
  title: 'Splat-Nav: Safe Real-Time Robot Navigation in Gaussian Splatting Maps'
  authors: Timothy Chen, Ola Shorinwa, Weijia Zeng, Joseph Bruno, Philip Dames, Mac
    Schwager
  year: '2024'
  abstract: We present Splat-Nav, a navigation pipeline that consists of a real-time
    safe planning module and a robust state estimation module designed to operate
    in the Gaussian Splatting (GSplat) environment representation, a popular emerging
    3D scene representation from computer vision. We formulate rigorous collision
    constraints that can be computed quickly to build a guaranteed-safe polytope corridor
    through the map. We then optimize a B-spline trajectory through this corridor.
    We also develop a real-time, robust state estimation module by interpreting the
    GSplat representation as a point cloud. The module enables the robot to localize
    its global pose with zero prior knowledge from RGB-D images using point cloud
    alignment, and then track its own pose as it moves through the scene from RGB
    images using image-to-point cloud localization. We also incorporate semantics
    into the GSplat in order to obtain better images for localization. All of these
    modules operate mainly on CPU, freeing up GPU resources for tasks like real-time
    scene reconstruction. We demonstrate the safety and robustness of our pipeline
    in both simulation and hardware, where we show re-planning at 5 Hz and pose estimation
    at 20 Hz, an order of magnitude faster than Neural Radiance Field (NeRF)-based
    navigation methods, thereby enabling real-time navigation.
  project_page: null
  paper: https://arxiv.org/pdf/2403.02751.pdf
  code: null
  video: null
  tags:
  - Robotics
  thumbnail: assets/thumbnails/chen2024splatnav.jpg
  publication_date: '2024-03-05T08:10:11+00:00'
- id: sun20233dgstream
  title: '3DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming of
    Photo-Realistic Free-Viewpoint Videos'
  authors: Jiakai Sun, Han Jiao, Guangyuan Li, Zhanjie Zhang, Lei Zhao, Wei Xing
  year: '2023'
  abstract: Constructing photo-realistic Free-Viewpoint Videos (FVVs) of dynamic scenes
    from multi-view videos remains a challenging endeavor. Despite the remarkable
    advancements achieved by current neural rendering techniques, these methods generally
    require complete video sequences for offline training and are not capable of real-time
    rendering. To address these constraints, we introduce 3DGStream, a method designed
    for efficient FVV streaming of real-world dynamic scenes. Our method achieves
    fast on-the-fly per-frame reconstruction within 12 seconds and real-time rendering
    at 200 FPS. Specificallggy, we utilize 3D Gaussians (3DGs) to represent the scene.
    Instead of the naïve approach of directly optimizing 3DGs per-frame, we employ
    a compact Neural Transformation Cache (NTC) to model the translations and rotations
    of 3DGs, markedly reducing the training time and storage required for each FVV
    frame. Furthermore, we propose an adaptive 3DG addition strategy to handle emerging
    objects in dynamic scenes. Experiments demonstrate that 3DGStream achieves competitive
    performance in terms of rendering speed, image quality, training time, and model
    storage when compared with state-of-the-art methods.
  project_page: https://sjojok.github.io/3dgstream/
  paper: https://arxiv.org/pdf/2403.01444.pdf
  code: https://github.com/SJoJoK/3DGStream
  video: null
  tags:
  - Code
  - Dynamic
  - Project
  thumbnail: assets/thumbnails/sun20233dgstream.jpg
  publication_date: '2024-03-03T08:42:40+00:00'
- id: lin2024vastgaussian
  title: 'VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction'
  authors: Jiaqi Lin, Zhihao Li, Xiao Tang, Jianzhuang Liu, Shiyong Liu, Jiayue Liu,
    Yangdi Lu, Xiaofei Wu, Songcen Xu, Youliang Yan, Wenming Yang
  year: '2024'
  abstract: Existing NeRF-based methods for large scene reconstruction often have
    limitations in visual quality and rendering speed. While the recent 3D Gaussian
    Splatting works well on small-scale and object-centric scenes, scaling it up to
    large scenes poses challenges due to limited video memory, long optimization time,
    and noticeable appearance variations. To address these challenges, we present
    VastGaussian, the first method for high-quality reconstruction and real-time rendering
    on large scenes based on 3D Gaussian Splatting. We propose a progressive partitioning
    strategy to divide a large scene into multiple cells, where the training cameras
    and point cloud are properly distributed with an airspace-aware visibility criterion.
    These cells are merged into a complete scene after parallel optimization. We also
    introduce decoupled appearance modeling into the optimization process to reduce
    appearance variations in the rendered images. Our approach outperforms existing
    NeRF-based methods and achieves state-of-the-art results on multiple large scene
    datasets, enabling fast optimization and high-fidelity real-time rendering.
  project_page: https://vastgaussian.github.io/
  paper: https://arxiv.org/pdf/2402.17427.pdf
  code: https://github.com/kangpeilun/VastGaussian
  video: null
  tags:
  - Large-Scale
  thumbnail: assets/thumbnails/lin2024vastgaussian.jpg
  publication_date: '2024-02-27T11:40:50+00:00'
- id: liu2024gva
  title: 'GVA: Reconstructing Vivid 3D Gaussian Avatars from Monocular Videos'
  authors: Xinqi Liu, Chenming Wu, Jialun Liu, Xing Liu, Jinbo Wu, Chen Zhao, Haocheng
    Feng, Errui Ding, Jingdong Wang
  year: '2024'
  abstract: In this paper, we present a novel method that facilitates the creation
    of vivid 3D Gaussian avatars from monocular video inputs (GVA). Our innovation
    lies in addressing the intricate challenges of delivering high-fidelity human
    body reconstructions and aligning 3D Gaussians with human skin surfaces accurately.
    The key contributions of this paper are twofold. Firstly, we introduce a pose
    refinement technique to improve hand and foot pose accuracy by aligning normal
    maps and silhouettes. Precise pose is crucial for correct shape and appearance
    reconstruction. Secondly, we address the problems of unbalanced aggregation and
    initialization bias that previously diminished the quality of 3D Gaussian avatars,
    through a novel surface-guided re-initialization method that ensures accurate
    alignment of 3D Gaussian points with avatar surfaces. Experimental results demonstrate
    that our proposed method achieves high-fidelity and vivid 3D Gaussian avatar reconstruction.
    Extensive experimental analyses validate the performance qualitatively and quantitatively,
    demonstrating that it achieves state-of-the-art performance in photo-realistic
    novel view synthesis while offering fine-grained control over the human body and
    hand pose.
  project_page: https://3d-aigc.github.io/GEA/
  paper: https://arxiv.org/pdf/2402.16607.pdf
  code: null
  video: null
  tags:
  - Avatar
  - Project
  thumbnail: assets/thumbnails/liu2024gva.jpg
  publication_date: '2024-02-26T14:40:15+00:00'
- id: yang2024specgaussian
  title: 'Spec-Gaussian: Anisotropic View-Dependent Appearance for 3D Gaussian Splatting'
  authors: Ziyi Yang, Xinyu Gao, Yangtian Sun, Yihua Huang, Xiaoyang Lyu, Wen Zhou,
    Shaohui Jiao, Xiaojuan Qi, Xiaogang Jin
  year: '2024'
  abstract: The recent advancements in 3D Gaussian splatting (3D-GS) have not only
    facilitated real-time rendering through modern GPU rasterization pipelines but
    have also attained state-of-the-art rendering quality. Nevertheless, despite its
    exceptional rendering quality and performance on standard datasets, 3D-GS frequently
    encounters difficulties in accurately modeling specular and anisotropic components.
    This issue stems from the limited ability of spherical harmonics (SH) to represent
    high-frequency information. To overcome this challenge, we introduce Spec-Gaussian,
    an approach that utilizes an anisotropic spherical Gaussian (ASG) appearance field
    instead of SH for modeling the view-dependent appearance of each 3D Gaussian.
    Additionally, we have developed a coarse-to-fine training strategy to improve
    learning efficiency and eliminate floaters caused by overfitting in real-world
    scenes. Our experimental results demonstrate that our method surpasses existing
    approaches in terms of rendering quality. Thanks to ASG, we have significantly
    improved the ability of 3D-GS to model scenes with specular and anisotropic components
    without increasing the number of 3D Gaussians. This improvement extends the applicability
    of 3D GS to handle intricate scenarios with specular and anisotropic surfaces.
  project_page: https://ingra14m.github.io/Spec-Gaussian-website/
  paper: https://arxiv.org/pdf/2402.15870.pdf
  code: https://github.com/ingra14m/Specular-Gaussians
  video: null
  tags:
  - Code
  - Project
  - Rendering
  thumbnail: assets/thumbnails/yang2024specgaussian.jpg
  publication_date: '2024-02-24T17:22:15+00:00'
- id: cheng2024gaussianpro
  title: 'GaussianPro: 3D Gaussian Splatting with Progressive Propagation'
  authors: Kai Cheng, Xiaoxiao Long, Kaizhi Yang, Yao Yao, Wei Yin, Yuexin Ma, Wenping
    Wang, Xuejin Chen
  year: '2024'
  abstract: The advent of 3D Gaussian Splatting (3DGS) has recently brought about
    a revolution in the field of neural rendering, facilitating high-quality renderings
    at real-time speed. However, 3DGS heavily depends on the initialized point cloud
    produced by Structure-from-Motion (SfM) techniques. When tackling with large-scale
    scenes that unavoidably contain texture-less surfaces, the SfM techniques always
    fail to produce enough points in these surfaces and cannot provide good initialization
    for 3DGS. As a result, 3DGS suffers from difficult optimization and low-quality
    renderings. In this paper, inspired by classical multi-view stereo (MVS) techniques,
    we propose GaussianPro, a novel method that applies a progressive propagation
    strategy to guide the densification of the 3D Gaussians. Compared to the simple
    split and clone strategies used in 3DGS, our method leverages the priors of the
    existing reconstructed geometries of the scene and patch matching techniques to
    produce new Gaussians with accurate positions and orientations. Experiments on
    both large-scale and small-scale scenes validate the effectiveness of our method,
    where our method significantly surpasses 3DGS on the Waymo dataset, exhibiting
    an improvement of 1.15dB in terms of PSNR.
  project_page: https://kcheng1021.github.io/gaussianpro.github.io/
  paper: https://arxiv.org/pdf/2402.14650.pdf
  code: https://github.com/kcheng1021/GaussianPro
  video: null
  tags:
  - Code
  - Densification
  - Project
  thumbnail: assets/thumbnails/cheng2024gaussianpro.jpg
  publication_date: '2024-02-22T16:00:20+00:00'
- id: tosi2024how
  title: 'How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey'
  authors: Fabio Tosi, Youmin Zhang, Ziren Gong, Erik Sandström, Stefano Mattoccia,
    Martin R. Oswald, Matteo Poggi
  year: '2024'
  abstract: Over the past two decades, research in the field of Simultaneous Localization
    and Mapping (SLAM) has undergone a significant evolution, highlighting its critical
    role in enabling autonomous exploration of unknown environments. This evolution
    ranges from hand-crafted methods, through the era of deep learning, to more recent
    developments focused on Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting
    (3DGS) representations. Recognizing the growing body of research and the absence
    of a comprehensive survey on the topic, this paper aims to provide the first comprehensive
    overview of SLAM progress through the lens of the latest advancements in radiance
    fields. It sheds light on the background, evolutionary path, inherent strengths
    and limitations, and serves as a fundamental reference to highlight the dynamic
    progress and specific challenges.
  project_page: null
  paper: https://arxiv.org/pdf/2402.13255.pdf
  code: null
  video: null
  tags:
  - Review
  thumbnail: assets/thumbnails/tosi2024how.jpg
  publication_date: '2024-02-20T18:59:57+00:00'
- id: luo2024gaussianhair
  title: 'GaussianHair: Hair Modeling and Rendering with Light-aware Gaussians'
  authors: Haimin Luo, Min Ouyang, Zijun Zhao, Suyi Jiang, Longwen Zhang, Qixuan Zhang,
    Wei Yang, Lan Xu, Jingyi Yu
  year: '2024'
  abstract: Hairstyle reflects culture and ethnicity at first glance. In the digital
    era, various realistic human hairstyles are also critical to high-fidelity digital
    human assets for beauty and inclusivity. Yet, realistic hair modeling and real-time
    rendering for animation is a formidable challenge due to its sheer number of strands,
    complicated structures of geometry, and sophisticated interaction with light.
    This paper presents GaussianHair, a novel explicit hair representation. It enables
    comprehensive modeling of hair geometry and appearance from images, fostering
    innovative illumination effects and dynamic animation capabilities. At the heart
    of GaussianHair is the novel concept of representing each hair strand as a sequence
    of connected cylindrical 3D Gaussian primitives. This approach not only retains
    the hair's geometric structure and appearance but also allows for efficient rasterization
    onto a 2D image plane, facilitating differentiable volumetric rendering. We further
    enhance this model with the "GaussianHair Scattering Model", adept at recreating
    the slender structure of hair strands and accurately capturing their local diffuse
    color in uniform lighting. Through extensive experiments, we substantiate that
    GaussianHair achieves breakthroughs in both geometric and appearance fidelity,
    transcending the limitations encountered in state-of-the-art methods for hair
    reconstruction. Beyond representation, GaussianHair extends to support editing,
    relighting, and dynamic rendering of hair, offering seamless integration with
    conventional CG pipeline workflows. Complementing these advancements, we have
    compiled an extensive dataset of real human hair, each with meticulously detailed
    strand geometry, to propel further research in this field.
  project_page: null
  paper: https://arxiv.org/pdf/2402.10483.pdf
  code: null
  video: null
  tags:
  - Avatar
  thumbnail: assets/thumbnails/luo2024gaussianhair.jpg
  publication_date: '2024-02-16T07:13:24+00:00'
- id: yang2024gaussianobject
  title: 'GaussianObject: Just Taking Four Images to Get A High-Quality 3D Object
    with Gaussian Splatting'
  authors: Chen Yang, Sikuang Li, Jiemin Fang, Ruofan Liang, Lingxi Xie, Xiaopeng
    Zhang, Wei Shen, Qi Tian
  year: '2024'
  abstract: 'Reconstructing and rendering 3D objects from highly sparse views is of
    critical importance for promoting applications of 3D vision techniques and improving
    user experience. However, images from sparse views only contain very limited 3D
    information, leading to two significant challenges: 1) Difficulty in building
    multi-view consistency as images for matching are too few; 2) Partially omitted
    or highly compressed object information as view coverage is insufficient. To tackle
    these challenges, we propose GaussianObject, a framework to represent and render
    the 3D object with Gaussian splatting, that achieves high rendering quality with
    only 4 input images. We first introduce techniques of visual hull and floater
    elimination which explicitly inject structure priors into the initial optimization
    process for helping build multi-view consistency, yielding a coarse 3D Gaussian
    representation. Then we construct a Gaussian repair model based on diffusion models
    to supplement the omitted object information, where Gaussians are further refined.
    We design a self-generating strategy to obtain image pairs for training the repair
    model. Our GaussianObject is evaluated on several challenging datasets, including
    MipNeRF360, OmniObject3D, and OpenIllumination, achieving strong reconstruction
    results from only 4 views and significantly outperforming previous state-of-the-art
    methods.'
  project_page: https://gaussianobject.github.io/
  paper: https://arxiv.org/pdf/2402.10259.pdf
  code: https://github.com/GaussianObject/GaussianObject
  video: https://youtu.be/ozoI0tmW3r0?si=KcaHtvVnrexqaf58
  tags:
  - Code
  - Diffusion
  - Project
  - Video
  thumbnail: assets/thumbnails/yang2024gaussianobject.jpg
  publication_date: '2024-02-15T18:42:33+00:00'
- id: hamdi2024ges
  title: 'GES: Generalized Exponential Splatting for Efficient Radiance Field Rendering'
  authors: Abdullah Hamdi, Luke Melas-Kyriazi, Guocheng Qian, Jinjie Mai, Ruoshi Liu,
    Carl Vondrick, Bernard Ghanem, Andrea Vedaldi
  year: '2024'
  abstract: Advancements in 3D Gaussian Splatting have significantly accelerated 3D
    reconstruction and generation. However, it may require a large number of Gaussians,
    which creates a substantial memory footprint. This paper introduces GES (Generalized
    Exponential Splatting), a novel representation that employs Generalized Exponential
    Function (GEF) to model 3D scenes, requiring far fewer particles to represent
    a scene and thus significantly outperforming Gaussian Splatting methods in efficiency
    with a plug-and-play replacement ability for Gaussian-based utilities. GES is
    validated theoretically and empirically in both principled 1D setup and realistic
    3D scenes. It is shown to represent signals with sharp edges more accurately,
    which are typically challenging for Gaussians due to their inherent low-pass characteristics.
    Our empirical analysis demonstrates that GEF outperforms Gaussians in fitting
    natural-occurring signals (e.g. squares, triangles, and parabolic signals), thereby
    reducing the need for extensive splitting operations that increase the memory
    footprint of Gaussian Splatting. With the aid of a frequency-modulated loss, GES
    achieves competitive performance in novel-view synthesis benchmarks while requiring
    less than half the memory storage of Gaussian Splatting and increasing the rendering
    speed by up to 39%.
  project_page: https://abdullahamdi.com/ges/
  paper: https://arxiv.org/pdf/2402.10128.pdf
  code: https://github.com/ajhamdi/ges-splatting
  video: https://youtu.be/edSvNy3roV8?si=VGncH7op1OfqkEtx
  tags:
  - Code
  - Project
  - Rendering
  - Video
  thumbnail: assets/thumbnails/hamdi2024ges.jpg
  publication_date: '2024-02-15T17:32:50+00:00'
- id: melaskyriazi2024im3d
  title: ' IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality
    3D Generation'
  authors: Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Natalia Neverova, Andrea
    Vedaldi, Oran Gafni, Filippos Kokkinos
  year: '2024'
  abstract: Most text-to-3D generators build upon off-the-shelf text-to-image models
    trained on billions of images. They use variants of Score Distillation Sampling
    (SDS), which is slow, somewhat unstable, and prone to artifacts. A mitigation
    is to fine-tune the 2D generator to be multi-view aware, which can help distillation
    or can be combined with reconstruction networks to output 3D objects directly.
    In this paper, we further explore the design space of text-to-3D models. We significantly
    improve multi-view generation by considering video instead of image generators.
    Combined with a 3D reconstruction algorithm which, by using Gaussian splatting,
    can optimize a robust image-based loss, we directly produce high-quality 3D outputs
    from the generated views. Our new method, IM-3D, reduces the number of evaluations
    of the 2D generator network 10-100x, resulting in a much more efficient pipeline,
    better quality, fewer geometric inconsistencies, and higher yield of usable 3D
    assets.
  project_page: https://lukemelas.github.io/IM-3D/
  paper: https://arxiv.org/pdf/2402.08682.pdf
  code: null
  video: null
  tags:
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/melaskyriazi2024im3d.jpg
  publication_date: '2024-02-13T18:59:51+00:00'
- id: zhou2024gala3d
  title: 'GALA3D: Towards Text-to-3D Complex Scene Generation via Layout-guided Generative
    Gaussian Splatting'
  authors: Xiaoyu Zhou, Xingjian Ran, Yajiao Xiong, Jinlin He, Zhiwei Lin, Yongtao
    Wang, Deqing Sun, Ming-Hsuan Yang
  year: '2024'
  abstract: We present GALA3D, generative 3D GAussians with LAyout-guided control,
    for effective compositional text-to-3D generation. We first utilize large language
    models (LLMs) to generate the initial layout and introduce a layout-guided 3D
    Gaussian representation for 3D content generation with adaptive geometric constraints.
    We then propose an object-scene compositional optimization mechanism with conditioned
    diffusion to collaboratively generate realistic 3D scenes with consistent geometry,
    texture, scale, and accurate interactions among multiple objects while simultaneously
    adjusting the coarse layout priors extracted from the LLMs to align with the generated
    scene. Experiments show that GALA3D is a user-friendly, end-to-end framework for
    state-of-the-art scene-level 3D content generation and controllable editing while
    ensuring the high fidelity of object-level entities within the scene.
  project_page: https://gala3d.github.io/
  paper: https://arxiv.org/pdf/2402.07207.pdf
  code: null
  video: null
  tags:
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/zhou2024gala3d.jpg
  publication_date: '2024-02-11T13:40:08+00:00'
- id: fei20243d
  title: '3D Gaussian as a New Vision Era: A Survey'
  authors: Ben Fei, Jingyi Xu, Rui Zhang, Qingyuan Zhou, Weidong Yang, Ying He
  year: '2024'
  abstract: 3D Gaussian Splatting (3D-GS) has emerged as a significant advancement
    in the field of Computer Graphics, offering explicit scene representation and
    novel view synthesis without the reliance on neural networks, such as Neural Radiance
    Fields (NeRF). This technique has found diverse applications in areas such as
    robotics, urban mapping, autonomous navigation, and virtual reality/augmented
    reality, just name a few. Given the growing popularity and expanding research
    in 3D Gaussian Splatting, this paper presents a comprehensive survey of relevant
    papers from the past year. We organize the survey into taxonomies based on characteristics
    and applications, providing an introduction to the theoretical underpinnings of
    3D Gaussian Splatting. Our goal through this survey is to acquaint new researchers
    with 3D Gaussian Splatting, serve as a valuable reference for seminal works in
    the field, and inspire future research directions, as discussed in our concluding
    section.
  project_page: null
  paper: https://arxiv.org/pdf/2402.07181.pdf
  code: null
  video: null
  tags:
  - Review
  thumbnail: assets/thumbnails/fei20243d.jpg
  publication_date: '2024-02-11T12:33:08+00:00'
- id: stanishevskii2024implicitdeepfake
  title: 'ImplicitDeepfake: Plausible Face-Swapping through Implicit Deepfake Generation
    using NeRF and Gaussian Splatting'
  authors: Georgii Stanishevskii, Jakub Steczkiewicz, Tomasz Szczepanik, Sławomir
    Tadeja, Jacek Tabor, Przemysław Spurek
  year: '2024'
  abstract: Numerous emerging deep-learning techniques have had a substantial impact
    on computer graphics. Among the most promising breakthroughs are the recent rise
    of Neural Radiance Fields (NeRFs) and Gaussian Splatting (GS). NeRFs encode the
    object's shape and color in neural network weights using a handful of images with
    known camera positions to generate novel views. In contrast, GS provides accelerated
    training and inference without a decrease in rendering quality by encoding the
    object's characteristics in a collection of Gaussian distributions. These two
    techniques have found many use cases in spatial computing and other domains. On
    the other hand, the emergence of deepfake methods has sparked considerable controversy.
    Such techniques can have a form of artificial intelligence-generated videos that
    closely mimic authentic footage. Using generative models, they can modify facial
    features, enabling the creation of altered identities or facial expressions that
    exhibit a remarkably realistic appearance to a real person. Despite these controversies,
    deepfake can offer a next-generation solution for avatar creation and gaming when
    of desirable quality. To that end, we show how to combine all these emerging technologies
    to obtain a more plausible outcome. Our ImplicitDeepfake1 uses the classical deepfake
    algorithm to modify all training images separately and then train NeRF and GS
    on modified faces. Such relatively simple strategies can produce plausible 3D
    deepfake-based avatars.
  project_page: null
  paper: https://arxiv.org/pdf/2402.06390.pdf
  code: null
  video: null
  tags:
  - Avatar
  thumbnail: assets/thumbnails/stanishevskii2024implicitdeepfake.jpg
  publication_date: '2024-02-09T13:11:57+00:00'
- id: zhou2024headstudio
  title: 'HeadStudio: Text to Animatable Head Avatars with 3D Gaussian Splatting'
  authors: Zhenglin Zhou, Fan Ma, Hehe Fan, Yi Yang
  year: '2024'
  abstract: 'Creating digital avatars from textual prompts has long been a desirable
    yet challenging task. Despite the promising outcomes obtained through 2D diffusion
    priors in recent works, current methods face challenges in achieving high-quality
    and animated avatars effectively. In this paper, we present HeadStudio, a novel
    framework that utilizes 3D Gaussian splatting to generate realistic and animated
    avatars from text prompts. Our method drives 3D Gaussians semantically to create
    a flexible and achievable appearance through the intermediate FLAME representation.
    Specifically, we incorporate the FLAME into both 3D representation and score distillation:
    1) FLAME-based 3D Gaussian splatting, driving 3D Gaussian points by rigging each
    point to a FLAME mesh. 2) FLAME-based score distillation sampling, utilizing FLAME-based
    fine-grained control signal to guide score distillation from the text prompt.
    Extensive experiments demonstrate the efficacy of HeadStudio in generating animatable
    avatars from textual prompts, exhibiting visually appealing appearances. The avatars
    are capable of rendering high-quality real-time (≥40 fps) novel views at a resolution
    of 1024. They can be smoothly controlled by real-world speech and video. We hope
    that HeadStudio can advance digital avatar creation and that the present method
    can widely be applied across various domains.'
  project_page: https://zhenglinzhou.github.io/HeadStudio-ProjectPage/
  paper: https://arxiv.org/pdf/2402.06149.pdf
  code: https://github.com/ZhenglinZhou/HeadStudio/
  video: https://zhenglinzhou.github.io/HeadStudio-ProjectPage/videos/demo_arxiv.mp4
  tags:
  - Avatar
  - Code
  - Project
  - Video
  thumbnail: assets/thumbnails/zhou2024headstudio.jpg
  publication_date: '2024-02-09T02:58:37+00:00'
- id: yang2024large
  title: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation
  authors: Chen Yang, Sikuang Li, Jiemin Fang, Ruofan Liang, Lingxi Xie, Xiaopeng
    Zhang, Wei Shen, Qi Tian
  year: '2024'
  abstract: '3D content creation has achieved significant progress in terms of both
    quality and speed. Although current feed-forward models can produce 3D objects
    in seconds, their resolution is constrained by the intensive computation required
    during training. In this paper, we introduce Large Multi-View Gaussian Model (LGM),
    a novel framework designed to generate high-resolution 3D models from text prompts
    or single-view images. Our key insights are two-fold: 1) 3D Representation: We
    propose multi-view Gaussian features as an efficient yet powerful representation,
    which can then be fused together for differentiable rendering. 2) 3D Backbone:
    We present an asymmetric U-Net as a high-throughput backbone operating on multi-view
    images, which can be produced from text or single-view image input by leveraging
    multi-view diffusion models. Extensive experiments demonstrate the high fidelity
    and efficiency of our approach. Notably, we maintain the fast speed to generate
    3D objects within 5 seconds while boosting the training resolution to 512, thereby
    achieving high-resolution 3D content generation.'
  project_page: https://me.kiui.moe/lgm/
  paper: https://arxiv.org/pdf/2402.05054.pdf
  code: https://github.com/3DTopia/LGM
  video: null
  tags:
  - Code
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/yang2024large.jpg
  publication_date: '2024-02-07T17:57:03+00:00'
- id: gao2024meshbased
  title: Mesh-based Gaussian Splatting for Real-time Large-scale Deformation
  authors: Lin Gao, Jie Yang, Bo-Tao Zhang, Jia-Mu Sun, Yu-Jie Yuan, Hongbo Fu, Yu-Kun
    Lai
  year: '2024'
  abstract: 'Neural implicit representations, including Neural Distance Fields and
    Neural Radiance Fields, have demonstrated significant capabilities for reconstructing
    surfaces with complicated geometry and topology, and generating novel views of
    a scene. Nevertheless, it is challenging for users to directly deform or manipulate
    these implicit representations with large deformations in the real-time fashion.
    Gaussian Splatting(GS) has recently become a promising method with explicit geometry
    for representing static scenes and facilitating high-quality and real-time synthesis
    of novel views. However,it cannot be easily deformed due to the use of discrete
    Gaussians and lack of explicit topology. To address this, we develop a novel GS-based
    method that enables interactive deformation. Our key idea is to design an innovative
    mesh-based GS representation, which is integrated into Gaussian learning and manipulation.
    3D Gaussians are defined over an explicit mesh, and they are bound with each other:
    the rendering of 3D Gaussians guides the mesh face split for adaptive refinement,
    and the mesh face split directs the splitting of 3D Gaussians. Moreover, the explicit
    mesh constraints help regularize the Gaussian distribution, suppressing poor-quality
    Gaussians(e.g. misaligned Gaussians,long-narrow shaped Gaussians), thus enhancing
    visual quality and avoiding artifacts during deformation. Based on this representation,
    we further introduce a large-scale Gaussian deformation technique to enable deformable
    GS, which alters the parameters of 3D Gaussians according to the manipulation
    of the associated mesh. Our method benefits from existing mesh deformation datasets
    for more realistic data-driven Gaussian deformation. Extensive experiments show
    that our approach achieves high-quality reconstruction and effective deformation,
    while maintaining the promising rendering results at a high frame rate(65 FPS
    on average).'
  project_page: null
  paper: https://arxiv.org/pdf/2402.04796.pdf
  code: null
  video: null
  tags:
  - Dynamic
  - Meshing
  thumbnail: assets/thumbnails/gao2024meshbased.jpg
  publication_date: '2024-02-07T12:36:54+00:00'
- id: rivero2024rig3dgs
  title: 'Rig3DGS: Creating Controllable Portraits from Casual Monocular Videos'
  authors: Alfredo Rivero, ShahRukh Athar, Zhixin Shu, Dimitris Samaras
  year: '2024'
  abstract: Creating controllable 3D human portraits from casual smartphone videos
    is highly desirable due to their immense value in AR/VR applications. The recent
    development of 3D Gaussian Splatting (3DGS) has shown improvements in rendering
    quality and training efficiency. However, it still remains a challenge to accurately
    model and disentangle head movements and facial expressions from a single-view
    capture to achieve high-quality renderings. In this paper, we introduce Rig3DGS
    to address this challenge. We represent the entire scene, including the dynamic
    subject, using a set of 3D Gaussians in a canonical space. Using a set of control
    signals, such as head pose and expressions, we transform them to the 3D space
    with learned deformations to generate the desired rendering. Our key innovation
    is a carefully designed deformation method which is guided by a learnable prior
    derived from a 3D morphable model. This approach is highly efficient in training
    and effective in controlling facial expressions, head positions, and view synthesis
    across various captures. We demonstrate the effectiveness of our learned deformation
    through extensive quantitative and qualitative experiments.
  project_page: https://shahrukhathar.github.io/2024/02/05/Rig3DGS.html
  paper: https://arxiv.org/pdf/2402.03723.pdf
  code: null
  video: null
  tags:
  - Avatar
  - Project
  thumbnail: assets/thumbnails/rivero2024rig3dgs.jpg
  publication_date: '2024-02-06T05:40:53+00:00'
- id: duan20244d
  title: '4D Gaussian Splatting: Towards Efficient Novel View Synthesis for Dynamic
    Scenes'
  authors: Yuanxing Duan, Fangyin Wei, Qiyu Dai, Yuhang He, Wenzheng Chen, Baoquan
    Chen
  year: '2024'
  abstract: We consider the problem of novel view synthesis (NVS) for dynamic scenes.
    Recent neural approaches have accomplished exceptional NVS results for static
    3D scenes, but extensions to 4D time-varying scenes remain non-trivial. Prior
    efforts often encode dynamics by learning a canonical space plus implicit or explicit
    deformation fields, which struggle in challenging scenarios like sudden movements
    or capturing high-fidelity renderings. In this paper, we introduce 4D Gaussian
    Splatting (4DGS), a novel method that represents dynamic scenes with anisotropic
    4D XYZT Gaussians, inspired by the success of 3D Gaussian Splatting in static
    scenes. We model dynamics at each timestamp by temporally slicing the 4D Gaussians,
    which naturally compose dynamic 3D Gaussians and can be seamlessly projected into
    images. As an explicit spatial-temporal representation, 4DGS demonstrates powerful
    capabilities for modeling complicated dynamics and fine details, especially for
    scenes with abrupt motions. We further implement our temporal slicing and splatting
    techniques in a highly optimized CUDA acceleration framework, achieving real-time
    inference rendering speeds of up to 277 FPS on an RTX 3090 GPU and 583 FPS on
    an RTX 4090 GPU. Rigorous evaluations on scenes with diverse motions showcase
    the superior efficiency and effectiveness of 4DGS, which consistently outperforms
    existing methods both quantitatively and qualitatively.
  project_page: https://weify627.github.io/4drotorgs/
  paper: https://arxiv.org/pdf/2402.03307.pdf
  code: https://github.com/weify627/4D-Rotor-Gaussians
  video: https://youtu.be/18zPuhQ3sSs
  tags:
  - Code
  - Dynamic
  - Project
  - Video
  thumbnail: assets/thumbnails/duan20244d.jpg
  publication_date: '2024-02-05T18:59:04+00:00'
- id: li2024sgsslam
  title: 'SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM'
  authors: Mingrui Li, Shuhong Liu, Heng Zhou
  year: '2024'
  abstract: Semantic understanding plays a crucial role in Dense Simultaneous Localization
    and Mapping (SLAM), facilitating comprehensive scene interpretation. Recent advancements
    that integrate Gaussian Splatting into SLAM systems have demonstrated its effectiveness
    in generating high-quality renderings through the use of explicit 3D Gaussian
    representations. Building on this progress, we propose SGS-SLAM, the first semantic
    dense visual SLAM system grounded in 3D Gaussians, which provides precise 3D semantic
    segmentation alongside high-fidelity reconstructions. Specifically, we propose
    to employ multi-channel optimization during the mapping process, integrating appearance,
    geometric, and semantic constraints with key-frame optimization to enhance reconstruction
    quality. Extensive experiments demonstrate that SGS-SLAM delivers state-of-the-art
    performance in camera pose estimation, map reconstruction, and semantic segmentation,
    outperforming existing methods meanwhile preserving real-time rendering ability.
  project_page: null
  paper: https://arxiv.org/pdf/2402.03246.pdf
  code: https://github.com/ShuhongLL/SGS-SLAM
  video: https://www.youtube.com/watch?v=y83yw1E-oUo
  tags:
  - Code
  - SLAM
  - Video
  thumbnail: assets/thumbnails/li2024sgsslam.jpg
  publication_date: '2024-02-05T18:03:53+00:00'
- id: waczyńska2024games
  title: 'GaMeS: Mesh-Based Adapting and Modification of Gaussian Splatting'
  authors: Joanna Waczyńska, Piotr Borycki, Sławomir Tadeja, Jacek Tabor, Przemysław
    Spurek
  year: '2024'
  abstract: In recent years, a range of neural network-based methods for image rendering
    have been introduced. For instance, widely-researched neural radiance fields (NeRF)
    rely on a neural network to represent 3D scenes, allowing for realistic view synthesis
    from a small number of 2D images. However, most NeRF models are constrained by
    long training and inference times. In comparison, Gaussian Splatting (GS) is a
    novel, state-of-theart technique for rendering points in a 3D scene by approximating
    their contribution to image pixels through Gaussian distributions, warranting
    fast training and swift, real-time rendering. A drawback of GS is the absence
    of a well-defined approach for its conditioning due to the necessity to condition
    several hundred thousand Gaussian components. To solve this, we introduce Gaussian
    Mesh Splatting (GaMeS) model, a hybrid of mesh and a Gaussian distribution, that
    pin all Gaussians splats on the object surface (mesh). The unique contribution
    of our methods is defining Gaussian splats solely based on their location on the
    mesh, allowing for automatic adjustments in position, scale, and rotation during
    animation. As a result, we obtain high-quality renders in the real-time generation
    of high-quality views. Furthermore, we demonstrate that in the absence of a predefined
    mesh, it is possible to fine-tune the initial mesh during the learning process.
  project_page: null
  paper: https://arxiv.org/pdf/2402.01459.pdf
  code: https://github.com/waczjoan/gaussian-mesh-splatting
  video: null
  tags:
  - Code
  - Dynamic
  - Meshing
  thumbnail: assets/thumbnails/waczyńska2024games.jpg
  publication_date: '2024-02-02T14:50:23+00:00'
- id: huang2024360gs
  title: '360-GS: Layout-guided Panoramic Gaussian Splatting For Indoor Roaming'
  authors: Letian Huang, Jiayang Bai, Jie Guo, Yanwen Guo
  year: '2024'
  abstract: 3D Gaussian Splatting (3D-GS) has recently attracted great attention with
    real-time and photo-realistic renderings. This technique typically takes perspective
    images as input and optimizes a set of 3D elliptical Gaussians by splatting them
    onto the image planes, resulting in 2D Gaussians. However, applying 3D-GS to panoramic
    inputs presents challenges in effectively modeling the projection onto the spherical
    surface of 360∘ images using 2D Gaussians. In practical applications, input panoramas
    are often sparse, leading to unreliable initialization of 3D Gaussians and subsequent
    degradation of 3D-GS quality. In addition, due to the under-constrained geometry
    of texture-less planes (e.g., walls and floors), 3D-GS struggles to model these
    flat regions with elliptical Gaussians, resulting in significant floaters in novel
    views. To address these issues, we propose 360-GS, a novel 360∘ Gaussian splatting
    for a limited set of panoramic inputs. Instead of splatting 3D Gaussians directly
    onto the spherical surface, 360-GS projects them onto the tangent plane of the
    unit sphere and then maps them to the spherical projections. This adaptation enables
    the representation of the projection using Gaussians. We guide the optimization
    of 360-GS by exploiting layout priors within panoramas, which are simple to obtain
    and contain strong structural information about the indoor scene. Our experimental
    results demonstrate that 360-GS allows panoramic rendering and outperforms state-of-the-art
    methods with fewer artifacts in novel view synthesis, thus providing immersive
    roaming in indoor scenarios.
  project_page: null
  paper: https://arxiv.org/pdf/2402.00763.pdf
  code: https://github.com/LeoDarcy/360GS
  video: null
  tags:
  - 360 degree
  - Code
  - Rendering
  thumbnail: assets/thumbnails/huang2024360gs.jpg
  publication_date: '2024-02-01T16:52:21+00:00'
- id: huang2024optimal
  title: 'On the Error Analysis of 3D Gaussian Splatting

    and an Optimal Projection Strategy

    '
  authors: Letian Huang, Jiayang Bai, Jie Guo, Yanwen Guo
  year: '2024'
  abstract: 3D Gaussian Splatting has garnered extensive attention and application
    in real-time neural rendering. Concurrently, concerns have been raised about the
    limitations of this technology in aspects such as point cloud storage, performance,
    and robustness in sparse viewpoints, leading to various improvements. However,
    there has been a notable lack of attention to the fundamental problem of projection
    errors introduced by the local affine approximation inherent in the splatting
    itself, and the consequential impact of these errors on the quality of photo-realistic
    rendering. This paper addresses the projection error function of 3D Gaussian Splatting,
    commencing with the residual error from the first-order Taylor expansion of the
    projection function. The analysis establishes a correlation between the error
    and the Gaussian mean position. Subsequently, leveraging function optimization
    theory, this paper analyzes the function's minima to provide an optimal projection
    strategy for Gaussian Splatting referred to Optimal Gaussian Splatting, which
    can accommodate a variety of camera models. Experimental validation further confirms
    that this projection methodology reduces artifacts, resulting in a more convincingly
    realistic rendering.
  project_page: https://letianhuang.github.io/op43dgs/
  paper: https://arxiv.org/pdf/2402.00752.pdf
  code: https://github.com/LetianHuang/op43dgs
  video: null
  tags:
  - Code
  - Perspective-correct
  - Project
  - Rendering
  thumbnail: assets/thumbnails/huang2024optimal.jpg
  publication_date: '2024-02-01T16:43:58+00:00'
- id: radl2024stopthepop
  title: 'StopThePop: Sorted Gaussian Splatting for View-Consistent Real-time Rendering'
  authors: Lukas Radl, Michael Steiner, Mathias Parger, Alexander Weinrauch, Bernhard
    Kerbl, Markus Steinberger
  year: '2024'
  abstract: Gaussian Splatting has emerged as a prominent model for constructing 3D
    representations from images across diverse domains. However, the efficiency of
    the 3D Gaussian Splatting rendering pipeline relies on several simplifications.
    Notably, reducing Gaussian to 2D splats with a single view-space depth introduces
    popping and blending artifacts during view rotation. Addressing this issue requires
    accurate per-pixel depth computation, yet a full per-pixel sort proves excessively
    costly compared to a global sort operation. In this paper, we present a novel
    hierarchical rasterization approach that systematically resorts and culls splats
    with minimal processing overhead. Our software rasterizer effectively eliminates
    popping artifacts and view inconsistencies, as demonstrated through both quantitative
    and qualitative measurements. Simultaneously, our method mitigates the potential
    for cheating view-dependent effects with popping, ensuring a more authentic representation.
    Despite the elimination of cheating, our approach achieves comparable quantitative
    results for test images, while increasing the consistency for novel view synthesis
    in motion. Due to its design, our hierarchical approach is only 4% slower on average
    than the original Gaussian Splatting. Notably, enforcing consistency enables a
    reduction in the number of Gaussians by approximately half with nearly identical
    quality and view-consistency. Consequently, rendering performance is nearly doubled,
    making our approach 1.6x faster than the original Gaussian Splatting, with a 50%
    reduction in memory requirements.
  project_page: https://r4dl.github.io/StopThePop/
  paper: https://arxiv.org/pdf/2402.00525.pdf
  code: https://github.com/r4dl/StopThePop
  video: https://youtu.be/EmcXtHYhigk
  tags:
  - Code
  - Perspective-correct
  - Project
  - Rendering
  - Video
  thumbnail: assets/thumbnails/radl2024stopthepop.jpg
  publication_date: '2024-02-01T11:46:44+00:00'
- id: hu2024segment
  title: Segment Anything in 3D Gaussians
  authors: Xu Hu, Yuxi Wang, Lue Fan, Junsong Fan, Junran Peng, Zhen Lei, Qing Li,
    Zhaoxiang Zhang
  year: '2024'
  abstract: 3D Gaussian Splatting has emerged as an alternative 3D representation
    of Neural Radiance Fields (NeRFs), benefiting from its high-quality rendering
    results and real-time rendering speed. Considering the 3D Gaussian representation
    remains unparsed, it is necessary first to execute object segmentation within
    this domain. Subsequently, scene editing and collision detection can be performed,
    proving vital to a multitude of applications, such as virtual reality (VR), augmented
    reality (AR), game/movie production, etc. In this paper, we propose a novel approach
    to achieve object segmentation in 3D Gaussian via an interactive procedure without
    any training process and learned parameters. We refer to the proposed method as
    SA-GS, for Segment Anything in 3D Gaussians. Given a set of clicked points in
    a single input view, SA-GS can generalize SAM to achieve 3D consistent segmentation
    via the proposed multi-view mask generation and view-wise label assignment methods.
    We also propose a cross-view label-voting approach to assign labels from different
    views. In addition, in order to address the boundary roughness issue of segmented
    objects resulting from the non-negligible spatial sizes of 3D Gaussian located
    at the boundary, SA-GS incorporates the simple but effective Gaussian Decomposition
    scheme. Extensive experiments demonstrate that SA-GS achieves high-quality 3D
    segmentation results, which can also be easily applied for scene editing and collision
    detection tasks.
  project_page: null
  paper: https://arxiv.org/pdf/2401.17857.pdf
  code: https://github.com/XuHu0529/SAGS
  video: null
  tags:
  - Code
  - Segmentation
  thumbnail: assets/thumbnails/hu2024segment.jpg
  publication_date: '2024-01-31T14:19:03+00:00'
- id: strong2024next
  title: 'Next Best Sense: Guiding Vision and Touch with FisherRF for 3D Gaussian
    Splatting'
  authors: Matthew Strong, Boshu Lei, Aiden Swann, Wen Jiang, Kostas Daniilidis, Monroe
    Kennedy III
  year: '2024'
  abstract: We propose a framework for active next best view and touch selection for
    robotic manipulators using 3D Gaussian Splatting (3DGS). 3DGS is emerging as a
    useful explicit 3D scene representation for robotics, as it has the ability to
    represent scenes in a both photorealistic and geometrically accurate manner. However,
    in real-world, online robotic scenes where the number of views is limited given
    efficiency requirements, random view selection for 3DGS becomes impractical as
    views are often overlapping and redundant. We address this issue by proposing
    an end-to-end online training and active view selection pipeline, which enhances
    the performance of 3DGS in few-view robotics settings. We first elevate the performance
    of few-shot 3DGS with a novel semantic depth alignment method using Segment Anything
    Model 2 (SAM2) that we supplement with Pearson depth and surface normal loss to
    improve color and depth reconstruction of real-world scenes. We then extend FisherRF,
    a next-best-view selection method for 3DGS, to select views and touch poses based
    on depth uncertainty. We perform online view selection on a real robot system
    during live 3DGS training. We motivate our improvements to few-shot GS scenes,
    and extend depth-based FisherRF to them, where we demonstrate both qualitative
    and quantitative improvements on challenging robot scenes.
  project_page: https://armlabstanford.github.io/next-best-sense
  paper: https://arxiv.org/pdf/2401.16663.pdf
  code: https://github.com/armlabstanford/NextBestSense
  video: https://www.youtube.com/watch?v=NHgb_16-Plg
  tags:
  - Code
  - Misc
  - Project
  - Robotics
  - Video
  thumbnail: assets/thumbnails/strong2024next.jpg
  publication_date: '2024-01-30T01:28:36+00:00'
- id: jiang2024vrgs
  title: 'VR-GS: A Physical Dynamics-Aware Interactive Gaussian Splatting System in
    Virtual Reality'
  authors: Ying Jiang, Chang Yu, Tianyi Xie, Xuan Li, Yutao Feng, Huamin Wang, Minchen
    Li, Henry Lau, Feng Gao, Yin Yang, Chenfanfu Jiang
  year: '2024'
  abstract: As consumer Virtual Reality (VR) and Mixed Reality (MR) technologies gain
    momentum, there's a growing focus on the development of engagements with 3D virtual
    content. Unfortunately, traditional techniques for content creation, editing,
    and interaction within these virtual spaces are fraught with difficulties. They
    tend to be not only engineering-intensive but also require extensive expertise,
    which adds to the frustration and inefficiency in virtual object manipulation.
    Our proposed VR-GS system represents a leap forward in human-centered 3D content
    interaction, offering a seamless and intuitive user experience. By developing
    a physical dynamics-aware interactive Gaussian Splatting in a Virtual Reality
    setting, and constructing a highly efficient two-level embedding strategy alongside
    deformable body simulations, VR-GS ensures real-time execution with highly realistic
    dynamic responses. The components of our Virtual Reality system are designed for
    high efficiency and effectiveness, starting from detailed scene reconstruction
    and object segmentation, advancing through multi-view image in-painting, and extending
    to interactive physics-based editing. The system also incorporates real-time deformation
    embedding and dynamic shadow casting, ensuring a comprehensive and engaging virtual
    experience.
  project_page: https://yingjiang96.github.io/VR-GS/
  paper: https://arxiv.org/pdf/2401.16663.pdf
  code: null
  video: null
  tags:
  - Meshing
  - Physics
  - Virtual Reality
  thumbnail: assets/thumbnails/jiang2024vrgs.jpg
  publication_date: '2024-01-30T01:28:36+00:00'
- id: huang2024endo4dgs
  title: 'Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting'
  authors: Yiming Huang, Beilei Cui, Long Bai, Ziqi Guo, Mengya Xu, Mobarakol Islam,
    Hongliang Ren
  year: '2024'
  abstract: 'In the realm of robot-assisted minimally invasive surgery, dynamic scene
    reconstruction can significantly enhance downstream tasks and improve surgical
    outcomes. Neural Radiance Fields (NeRF)-based methods have recently risen to prominence
    for their exceptional ability to reconstruct scenes but are hampered by slow inference
    speed, prolonged training, and inconsistent depth estimation. Some previous work
    utilizes ground truth depth for optimization but is hard to acquire in the surgical
    domain. To overcome these obstacles, we present Endo-4DGS, a real-time endoscopic
    dynamic reconstruction approach that utilizes 3D Gaussian Splatting (GS) for 3D
    representation. Specifically, we propose lightweight MLPs to capture temporal
    dynamics with Gaussian deformation fields. To obtain a satisfactory Gaussian Initialization,
    we exploit a powerful depth estimation foundation model, Depth-Anything, to generate
    pseudo-depth maps as a geometry prior. We additionally propose confidence-guided
    learning to tackle the ill-pose problems in monocular depth estimation and enhance
    the depth-guided reconstruction with surface normal constraints and depth regularization.
    Our approach has been validated on two surgical datasets, where it can effectively
    render in real-time, compute efficiently, and reconstruct with remarkable accuracy.

    '
  project_page: null
  paper: https://arxiv.org/pdf/2401.16416.pdf
  code: null
  video: null
  tags:
  - Dynamic
  - Medicine
  thumbnail: assets/thumbnails/huang2024endo4dgs.jpg
  publication_date: '2024-01-29T18:55:29+00:00'
  date_source: arxiv
- id: feng2024gaussian
  title: 'Gaussian Splashing: Dynamic Fluid Synthesis with Gaussian Splatting'
  authors: Yutao Feng, Xiang Feng, Yintong Shang, Ying Jiang, Chang Yu, Zeshun Zong,
    Tianjia Shao, Hongzhi Wu, Kun Zhou, Chenfanfu Jiang, Yin Yang
  year: '2024'
  abstract: We demonstrate the feasibility of integrating physics-based animations
    of solids and fluids with 3D Gaussian Splatting (3DGS) to create novel effects
    in virtual scenes reconstructed using 3DGS. Leveraging the coherence of the Gaussian
    splatting and position-based dynamics (PBD) in the underlying representation,
    we manage rendering, view synthesis, and the dynamics of solids and fluids in
    a cohesive manner. Similar to Gaussian shader, we enhance each Gaussian kernel
    with an added normal, aligning the kernel's orientation with the surface normal
    to refine the PBD simulation. This approach effectively eliminates spiky noises
    that arise from rotational deformation in solids. It also allows us to integrate
    physically based rendering to augment the dynamic surface reflections on fluids.
    Consequently, our framework is capable of realistically reproducing surface highlights
    on dynamic fluids and facilitating interactions between scene objects and fluids
    from new views.
  project_page: https://amysteriouscat.github.io/GaussianSplashing/
  paper: https://arxiv.org/pdf/2401.15318.pdf
  code: null
  video: https://www.youtube.com/watch?v=KgaR1ni-Egg&t
  tags:
  - Dynamic
  - Physics
  - Project
  - Rendering
  - Video
  thumbnail: assets/thumbnails/feng2024gaussian.jpg
  publication_date: '2024-01-27T06:45:22+00:00'
- id: hong2024livgaussmap
  title: 'LIV-GaussMap: LiDAR-Inertial-Visual Fusion for Real-time 3D Radiance Field
    Map Rendering'
  authors: Sheng Hong, Junjie He, Xinhu Zheng, Hesheng Wang, Hao Fang, Kangcheng Liu,
    Chunran Zheng, Shaojie Shen
  year: '2024'
  abstract: We introduce an integrated precise LiDAR, Inertial, and Visual (LIV) multi-modal
    sensor fused mapping system that builds on the differentiable surface splatting
    to improve the mapping fidelity, quality, and structural accuracy. Notably, this
    is also a novel form of tightly coupled map for LiDAR-visual-inertial sensor fusion.
    This system leverages the complementary characteristics of LiDAR and visual data
    to capture the geometric structures of large-scale 3D scenes and restore their
    visual surface information with high fidelity. The initial poses for surface Gaussian
    scenes are obtained using a LiDAR-inertial system with size-adaptive voxels. Then,
    we optimized and refined the Gaussians by visual-derived photometric gradients
    to optimize the quality and density of LiDAR measurements. Our method is compatible
    with various types of LiDAR, including solid-state and mechanical LiDAR, supporting
    both repetitive and non-repetitive scanning modes. bolstering structure construction
    through LiDAR and facilitating real-time generation of photorealistic renderings
    across diverse LIV datasets. It showcases notable resilience and versatility in
    generating real-time photorealistic scenes potentially for digital twins and virtual
    reality while also holding potential applicability in real-time SLAM and robotics
    domains.
  project_page: null
  paper: https://arxiv.org/pdf/2401.14857.pdf
  code: null
  video: null
  tags:
  - Large-Scale
  - Lidar
  thumbnail: assets/thumbnails/hong2024livgaussmap.jpg
  publication_date: '2024-01-26T13:36:46+00:00'
- id: zhuang2024tipeditor
  title: 'TIP-Editor: An Accurate 3D Editor Following Both Text-Prompts And Image-Prompts'
  authors: Jingyu Zhuang, Di Kang, Yan-Pei Cao, Guanbin Li, Liang Lin, Ying Shan
  year: '2024'
  abstract: Text-driven 3D scene editing has gained significant attention owing to
    its convenience and user-friendliness. However, existing methods still lack accurate
    control of the specified appearance and location of the editing result due to
    the inherent limitations of the text description. To this end, we propose a 3D
    scene editing framework, TIPEditor, that accepts both text and image prompts and
    a 3D bounding box to specify the editing region. With the image prompt, users
    can conveniently specify the detailed appearance/style of the target content in
    complement to the text description, enabling accurate control of the appearance.
    Specifically, TIP-Editor employs a stepwise 2D personalization strategy to better
    learn the representation of the existing scene and the reference image, in which
    a localization loss is proposed to encourage correct object placement as specified
    by the bounding box. Additionally, TIPEditor utilizes explicit and flexible 3D
    Gaussian splatting as the 3D representation to facilitate local editing while
    keeping the background unchanged. Extensive experiments have demonstrated that
    TIP-Editor conducts accurate editing following the text and image prompts in the
    specified bounding box region, consistently outperforming the baselines in editing
    quality, and the alignment to the prompts, qualitatively and quantitatively.
  project_page: https://zjy526223908.github.io/TIP-Editor/
  paper: https://arxiv.org/pdf/2401.14828.pdf
  code: https://github.com/zjy526223908/TIP-Editor
  video: https://zjy526223908.github.io/TIP-Editor/static/videos/TIP_editor_video_short.mp4
  tags:
  - Code
  - Editing
  - Project
  - Video
  thumbnail: assets/thumbnails/zhuang2024tipeditor.jpg
  publication_date: '2024-01-26T12:57:05+00:00'
- id: xiong2024gauuscene
  title: 'GauU-Scene: A Scene Reconstruction Benchmark on Large Scale 3D Reconstruction
    Dataset Using Gaussian Splatting'
  authors: Butian Xiong, Zhuo Li, Zhen Li
  year: '2024'
  abstract: We introduce a novel large-scale scene reconstruction benchmark using
    the newly developed 3D representation approach, Gaussian Splatting, on our expansive
    U-Scene dataset. U-Scene encompasses over one and a half square kilometres, featuring
    a comprehensive RGB dataset coupled with LiDAR ground truth. For data acquisition,
    we employed the Matrix 300 drone equipped with the high-accuracy Zenmuse L1 LiDAR,
    enabling precise rooftop data collection. This dataset, offers a unique blend
    of urban and academic environments for advanced spatial analysis convers more
    than 1.5 km2. Our evaluation of U-Scene with Gaussian Splatting includes a detailed
    analysis across various novel viewpoints. We also juxtapose these results with
    those derived from our accurate point cloud dataset, highlighting significant
    differences that underscore the importance of combine multi-modal information
  project_page: https://saliteta.github.io/CUHKSZ_SMBU/
  paper: https://arxiv.org/pdf/2401.14032.pdf
  code: https://github.com/saliteta/lidar_SfM_alignment
  video: null
  tags:
  - Code
  - Large-Scale
  - Project
  thumbnail: assets/thumbnails/xiong2024gauuscene.jpg
  publication_date: '2024-01-25T09:22:32+00:00'
- id: zhao2024psavatar
  title: 'PSAvatar: A Point-based Morphable Shape Model for Real-Time Head Avatar
    Creation with 3D Gaussian Splatting'
  authors: Zhongyuan Zhao, Zhenyu Bao, Qing Li, Guoping Qiu, Kanglin Liu
  year: '2024'
  abstract: Despite much progress, achieving real-time high-fidelity head avatar animation
    is still difficult and existing methods have to trade-off between speed and quality.
    3DMM based methods often fail to model non-facial structures such as eyeglasses
    and hairstyles, while neural implicit models suffer from deformation inflexibility
    and rendering inefficiency. Although 3D Gaussian has been demonstrated to possess
    promising capability for geometry representation and radiance field reconstruction,
    applying 3D Gaussian in head avatar creation remains a major challenge since it
    is difficult for 3D Gaussian to model the head shape variations caused by changing
    poses and expressions. In this paper, we introduce PSAvatar, a novel framework
    for animatable head avatar creation that utilizes discrete geometric primitive
    to create a parametric morphable shape model and employs 3D Gaussian for fine
    detail representation and high fidelity rendering. The parametric morphable shape
    model is a Point-based Morphable Shape Model (PMSM) which uses points instead
    of meshes for 3D representation to achieve enhanced representation flexibility.
    The PMSM first converts the FLAME mesh to points by sampling on the surfaces as
    well as off the meshes to enable the reconstruction of not only surface-like structures
    but also complex geometries such as eyeglasses and hairstyles. By aligning these
    points with the head shape in an analysis-by-synthesis manner, the PMSM makes
    it possible to utilize 3D Gaussian for fine detail representation and appearance
    modeling, thus enabling the creation of high-fidelity avatars. We show that PSAvatar
    can reconstruct high-fidelity head avatars of a variety of subjects and the avatars
    can be animated in real-time (≥ 25 fps at a resolution of 512 × 512 ).
  project_page: null
  paper: https://arxiv.org/pdf/2401.12900.pdf
  code: null
  video: null
  tags:
  - Avatar
  thumbnail: assets/thumbnails/zhao2024psavatar.jpg
  publication_date: '2024-01-23T16:40:47+00:00'
- id: liu2024endogaussian
  title: 'EndoGaussian: Gaussian Splatting for Deformable Surgical Scene Reconstruction'
  authors: Yifan Liu, Chenxin Li, Chen Yang, Yixuan Yuan
  year: '2024'
  abstract: Reconstructing deformable tissues from endoscopic stereo videos is essential
    in many downstream surgical applications. However, existing methods suffer from
    slow inference speed, which greatly limits their practical use. In this paper,
    we introduce EndoGaussian, a real-time surgical scene reconstruction framework
    that builds on 3D Gaussian Splatting. Our framework represents dynamic surgical
    scenes as canonical Gaussians and a time-dependent deformation field, which predicts
    Gaussian deformations at novel timestamps. Due to the efficient Gaussian representation
    and parallel rendering pipeline, our framework significantly accelerates the rendering
    speed compared to previous methods. In addition, we design the deformation field
    as the combination of a lightweight encoding voxel and an extremely tiny MLP,
    allowing for efficient Gaussian tracking with a minor rendering burden. Furthermore,
    we design a holistic Gaussian initialization method to fully leverage the surface
    distribution prior, achieved by searching informative points from across the input
    image sequence. Experiments on public endoscope datasets demonstrate that our
    method can achieve real-time rendering speed (195 FPS real-time, 100× gain) while
    maintaining the state-of-the-art reconstruction quality (35.925 PSNR) and the
    fastest training speed (within 2 min/scene), showing significant promise for intraoperative
    surgery applications.
  project_page: https://yifliu3.github.io/EndoGaussian/
  paper: https://arxiv.org/pdf/2401.12561.pdf
  code: https://github.com/CUHK-AIM-Group/EndoGaussian
  video: null
  tags:
  - Code
  - Project
  thumbnail: assets/thumbnails/liu2024endogaussian.jpg
  publication_date: '2024-01-23T08:44:26+00:00'
- id: zhu2024endogs
  title: 'EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting'
  authors: Lingting Zhu, Zhao Wang, Jiahao Cui, Zhenchao Jin, Guying Lin, Lequan Yu
  year: '2024'
  abstract: Surgical 3D reconstruction is a critical area of research in robotic surgery,
    with recent works adopting variants of dynamic radiance fields to achieve success
    in 3D reconstruction of deformable tissues from single-viewpoint videos. However,
    these methods often suffer from time-consuming optimization or inferior quality,
    limiting their adoption in downstream tasks. Inspired by 3D Gaussian Splatting,
    a recent trending 3D representation, we present EndoGS, applying Gaussian Splatting
    for deformable endoscopic tissue reconstruction. Specifically, our approach incorporates
    deformation fields to handle dynamic scenes, depth-guided supervision with spatial-temporal
    weight masks to optimize 3D targets with tool occlusion from a single viewpoint,
    and surface-aligned regularization terms to capture the much better geometry.
    As a result, EndoGS reconstructs and renders high-quality deformable endoscopic
    tissues from a single-viewpoint video, estimated depth maps, and labeled tool
    masks. Experiments on DaVinci robotic surgery videos demonstrate that EndoGS achieves
    superior rendering quality.
  project_page: null
  paper: https://arxiv.org/pdf/2401.11535.pdf
  code: https://github.com/HKU-MedAI/EndoGS
  video: null
  tags:
  - Code
  - Medicine
  thumbnail: assets/thumbnails/zhu2024endogs.jpg
  publication_date: '2024-01-21T16:14:04+00:00'
- id: li2024gaussianbody
  title: 'GaussianBody: Clothed Human Reconstruction via 3d Gaussian Splatting'
  authors: Mengtian Li, Shengxiang Yao, Zhifeng Xie, Keyu Chen, Yu-Gang Jiang
  year: '2024'
  abstract: In this work, we propose a novel clothed human reconstruction method called
    GaussianBody, based on 3D Gaussian Splatting. Compared with the costly neural
    radiance based models, 3D Gaussian Splatting has recently demonstrated great performance
    in terms of training time and rendering quality. However, applying the static
    3D Gaussian Splatting model to the dynamic human reconstruction problem is non-trivial
    due to complicated non-rigid deformations and rich cloth details. To address these
    challenges, our method considers explicit pose-guided deformation to associate
    dynamic Gaussians across the canonical space and the observation space, introducing
    a physically-based prior with regularized transformations helps mitigate ambiguity
    between the two spaces. During the training process, we further propose a pose
    refinement strategy to update the pose regression for compensating the inaccurate
    initial estimation and a split-with-scale mechanism to enhance the density of
    regressed point clouds. The experiments validate that our method can achieve state-of-the-art
    photorealistic novel-view rendering results with high-quality details for dynamic
    clothed human bodies, along with explicit geometry reconstruction.
  project_page: null
  paper: https://arxiv.org/pdf/2401.09720.pdf
  code: null
  video: null
  tags:
  - Avatar
  thumbnail: assets/thumbnails/li2024gaussianbody.jpg
  publication_date: '2024-01-18T04:48:13+00:00'
- id: pan2024fast
  title: Fast Dynamic 3D Object Generation from a Single-view Video
  authors: Zijie Pan, Zeyu Yang, Xiatian Zhu, Li Zhang
  year: '2024'
  abstract: Generating dynamic three-dimensional (3D) object from a single-view video
    is challenging due to the lack of 4D labeled data. Existing methods extend text-to-3D
    pipelines by transferring off-the-shelf image generation models such as score
    distillation sampling, but they are slow and expensive to scale (e.g., 150 minutes
    per object) due to the need for back-propagating the information-limited supervision
    signals through a large pretrained model. To address this limitation, we propose
    an efficient video-to-4D object generation framework called Efficient4D. It generates
    high-quality spacetime-consistent images under different camera views, and then
    uses them as labeled data to directly train a novel 4D Gaussian splatting model
    with explicit point cloud geometry, enabling real-time rendering under continuous
    camera trajectories. Extensive experiments on synthetic and real videos show that
    Efficient4D offers a remarkable 10-fold increase in speed when compared to prior
    art alternatives while preserving the same level of innovative view synthesis
    quality. For example, Efficient4D takes only 14 minutes to model a dynamic object.
  project_page: https://fudan-zvg.github.io/Efficient4D/
  paper: https://arxiv.org/pdf/2401.08742.pdf
  code: https://github.com/fudan-zvg/Efficient4D
  video: https://fudan-zvg.github.io/Efficient4D/assets/video/demo.mp4
  tags:
  - Code
  - Diffusion
  - Project
  - Video
  thumbnail: assets/thumbnails/pan2024fast.jpg
  publication_date: '2024-01-16T18:58:36+00:00'
- id: bolanos2024gaussian
  title: Gaussian Shadow Casting for Neural Characters
  authors: Luis Bolanos, Shih-Yang Su, Helge Rhodin
  year: '2024'
  abstract: Neural character models can now reconstruct detailed geometry and texture
    from video, but they lack explicit shadows and shading, leading to artifacts when
    generating novel views and poses or during relighting. It is particularly difficult
    to include shadows as they are a global effect and the required casting of secondary
    rays is costly. We propose a new shadow model using a Gaussian density proxy that
    replaces sampling with a simple analytic formula. It supports dynamic motion and
    is tailored for shadow computation, thereby avoiding the affine projection approximation
    and sorting required by the closely related Gaussian splatting. Combined with
    a deferred neural rendering model, our Gaussian shadows enable Lambertian shading
    and shadow casting with minimal overhead. We demonstrate improved reconstructions,
    with better separation of albedo, shading, and shadows in challenging outdoor
    scenes with direct sun light and hard shadows. Our method is able to optimize
    the light direction without any input from the user. As a result, novel poses
    have fewer shadow artifacts and relighting in novel scenes is more realistic compared
    to the state-of-the-art methods, providing new ways to pose neural characters
    in novel environments, increasing their applicability.
  project_page: null
  paper: https://arxiv.org/pdf/2401.06116.pdf
  code: https://github.com/LuisBolanos17/GaussianShadowCasting
  video: null
  tags:
  - Avatar
  - Code
  - Relight
  - Rendering
  thumbnail: assets/thumbnails/bolanos2024gaussian.jpg
  publication_date: '2024-01-11T18:50:31+00:00'
- id: dou2024cosseggaussians
  title: 'CoSSegGaussians: Compact and Swift Scene Segmenting 3D Gaussians'
  authors: Bin Dou, Tianyu Zhang, Yongjia Ma, Zhaohui Wang, Zejian Yuan
  year: '2024'
  abstract: 'We propose Compact and Swift Segmenting 3D Gaussians(CoSSegGaussians),
    a method for compact 3D-consistent scene segmentation at fast rendering speed
    with only RGB images input. Previous NeRF-based 3D segmentation methods have relied
    on implicit or voxel neural scene representation and ray-marching volume rendering
    which are time consuming. Recent 3D Gaussian Splatting significantly improves
    the rendering speed, however, existing Gaussians-based segmentation methods(eg:
    Gaussian Grouping) fail to provide compact segmentation masks especially in zero-shot
    segmentation, which is mainly caused by the lack of robustness and compactness
    for straightforwardly assigning learnable parameters to each Gaussian when encountering
    inconsistent 2D machine-generated labels. Our method aims to achieve compact and
    reliable zero-shot scene segmentation swiftly by mapping fused spatial and semantically
    meaningful features for each Gaussian point with a shallow decoding network. Specifically,
    our method firstly optimizes Gaussian points’ position, convariance and color
    attributes under the supervision of RGB images. After Gaussian Locating, we distill
    multi-scale DINO features extracted from images through unprojection to each Gaussian,
    which is then incorporated with spatial features from the fast point features
    processing network, i.e. RandLA-Net. Then the shallow decoding MLP is applied
    to the multi-scale fused features to obtain compact segmentation. Experimental
    results show that our model can perform high-quality zero-shot scene segmentation,
    as our model outperforms other segmentation methods on both semantic and panoptic
    segmentation task, meanwhile consumes approximately only 10% segmenting time compared
    to NeRF-based segmentation.'
  project_page: https://david-dou.github.io/CoSSegGaussians/
  paper: https://arxiv.org/pdf/2401.05925.pdf
  code: null
  video: null
  tags:
  - Project
  - Segmentation
  thumbnail: assets/thumbnails/dou2024cosseggaussians.jpg
  publication_date: '2024-01-11T14:05:01+00:00'
- id: xu2024agg
  title: 'AGG: Amortized Generative 3D Gaussians for Single Image to 3D'
  authors: Dejia Xu, Ye Yuan, Morteza Mardani, Sifei Liu, Jiaming Song, Zhangyang
    Wang, Arash Vahdat
  year: '2024'
  abstract: Given the growing need for automatic 3D content creation pipelines, various
    3D representations have been studied to generate 3D objects from a single image.
    Due to its superior rendering efficiency, 3D Gaussian splatting-based models have
    recently excelled in both 3D reconstruction and generation. 3D Gaussian splatting
    approaches for image to 3D generation are often optimization-based, requiring
    many computationally expensive score-distillation steps. To overcome these challenges,
    we introduce an Amortized Generative 3D Gaussian framework (AGG) that instantly
    produces 3D Gaussians from a single image, eliminating the need for per-instance
    optimization. Utilizing an intermediate hybrid representation, AGG decomposes
    the generation of 3D Gaussian locations and other appearance attributes for joint
    optimization. Moreover, we propose a cascaded pipeline that first generates a
    coarse representation of the 3D data and later upsamples it with a 3D Gaussian
    super-resolution module. Our method is evaluated against existing optimization-based
    3D Gaussian frameworks and sampling-based pipelines utilizing other 3D representations,
    where AGG showcases competitive generation abilities both qualitatively and quantitatively
    while being several orders of magnitude faster.
  project_page: https://ir1d.github.io/AGG/
  paper: https://arxiv.org/pdf/2401.04099.pdf
  code: null
  video: https://youtu.be/jkwmp2UH0Ug?si=lBXjme-d9bVrXTNf
  tags:
  - Diffusion
  - Project
  - Video
  thumbnail: assets/thumbnails/xu2024agg.jpg
  publication_date: '2024-01-08T18:56:33+00:00'
- id: chen2024a
  title: A Survey on 3D Gaussian Splatting
  authors: Guikun Chen, Wenguan Wang
  year: '2024'
  abstract: 3D Gaussian splatting (3D GS) has recently emerged as a transformative
    technique in the explicit radiance field and computer graphics landscape. This
    innovative approach, characterized by the utilization of millions of 3D Gaussians,
    represents a significant departure from the neural radiance field (NeRF) methodologies,
    which predominantly use implicit, coordinate-based models to map spatial coordinates
    to pixel values. 3D GS, with its explicit scene representations and differentiable
    rendering algorithms, not only promises real-time rendering capabilities but also
    introduces unprecedented levels of control and editability. This positions 3D
    GS as a potential game-changer for the next generation of 3D reconstruction and
    representation. In the present paper, we provide the first systematic overview
    of the recent developments and critical contributions in the domain of 3D GS.
    We begin with a detailed exploration of the underlying principles and the driving
    forces behind the advent of 3D GS, setting the stage for understanding its significance.
    A focal point of our discussion is the practical applicability of 3D GS. By facilitating
    real-time performance, 3D GS opens up a plethora of applications, ranging from
    virtual reality to interactive media and beyond. This is complemented by a comparative
    analysis of leading 3D GS models, evaluated across various benchmark tasks to
    highlight their performance and practical utility. The survey concludes by identifying
    current challenges and suggesting potential avenues for future research in this
    domain. Through this survey, we aim to provide a valuable resource for both newcomers
    and seasoned researchers, fostering further exploration and advancement in applicable
    and explicit radiance field representation.
  project_page: null
  paper: https://arxiv.org/pdf/2401.03890.pdf
  code: null
  video: null
  tags:
  - Review
  thumbnail: assets/thumbnails/chen2024a.jpg
  publication_date: '2024-01-08T13:42:59+00:00'
- id: bai2024progress
  title: 'Progress and Prospects in 3D Generative AI: A Technical Overview including
    3D human'
  authors: Song Bai, Jie Li
  year: '2024'
  abstract: While AI-generated text and 2D images continue to expand its territory,
    3D generation has gradually emerged as a trend that cannot be ignored. Since the
    year 2023 an abundant amount of research papers has emerged in the domain of 3D
    generation. This growth encompasses not just the creation of 3D objects, but also
    the rapid development of 3D character and motion generation. Several key factors
    contribute to this progress. The enhanced fidelity in stable diffusion, coupled
    with control methods that ensure multi-view consistency, and realistic human models
    like SMPL-X, contribute synergistically to the production of 3D models with remarkable
    consistency and near-realistic appearances. The advancements in neural network-based
    3D storing and rendering models, such as Neural Radiance Fields (NeRF) and 3D
    Gaussian Splatting (3DGS), have accelerated the efficiency and realism of neural
    rendered models. Furthermore, the multimodality capabilities of large language
    models have enabled language inputs to transcend into human motion outputs. This
    paper aims to provide a comprehensive overview and summary of the relevant papers
    published mostly during the latter half year of 2023. It will begin by discussing
    the AI generated object models in 3D, followed by the generated 3D human models,
    and finally, the generated 3D human motions, culminating in a conclusive summary
    and a vision for the future.
  project_page: null
  paper: https://arxiv.org/pdf/2401.02620.pdf
  code: null
  video: null
  tags:
  - Avatar
  - Review
  thumbnail: assets/thumbnails/bai2024progress.jpg
  publication_date: '2024-01-05T03:41:38+00:00'
- id: meyer2023pegasus
  title: 'PEGASUS: Physically Enhanced Gaussian Splatting Simulation System for 6DOF
    Object Pose Dataset Generation'
  authors: Lukas Meyer, Floris Erich, Yusuke Yoshiyasu, Marc Stamminger, Noriaki Ando,
    Yukiyasu Domae
  year: '2023'
  abstract: Modeling dynamic, large-scale urban scenes is challenging due to their
    highly intricate geometric structures and unconstrained dynamics in both space
    and time. Prior methods often employ high-level architectural priors, separating
    static and dynamic elements, resulting in suboptimal capture of their synergistic
    interactions. To address this challenge, we present a unified representation model,
    called Periodic Vibration Gaussian (PVG). PVG builds upon the efficient 3D Gaussian
    splatting technique, originally designed for static scene representation, by introducing
    periodic vibration-based temporal dynamics. This innovation enables PVG to elegantly
    and uniformly represent the characteristics of various objects and elements in
    dynamic urban scenes. To enhance temporally coherent representation learning with
    sparse training data, we introduce a novel flow-based temporal smoothing mechanism
    and a position-aware adaptive control strategy. Extensive experiments on Waymo
    Open Dataset and KITTI benchmarks demonstrate that PVG surpasses state-of-the-art
    alternatives in both reconstruction and novel view synthesis for both dynamic
    and static scenes. Notably, PVG achieves this without relying on manually labeled
    object bounding boxes or expensive optical flow estimation. Moreover, PVG exhibits
    50/6000-fold acceleration in training/rendering over the best alternative.
  project_page: https://meyerls.github.io/pegasus_web/
  paper: https://arxiv.org/pdf/2401.02281.pdf
  code: https://github.com/meyerls/PEGASUS
  video: null
  tags:
  - Code
  - Misc
  - Project
  thumbnail: assets/thumbnails/meyer2023pegasus.jpg
  publication_date: '2024-01-04T13:58:14+00:00'
- id: zuo2024fmgs
  title: 'FMGS: Foundation Model Embedded 3D Gaussian Splatting for Holistic 3D Scene
    Understanding'
  authors: Xingxing Zuo, Pouya Samangouei, Yunwen Zhou, Yan Di, Mingyang Li
  year: '2024'
  abstract: Precisely perceiving the geometric and semantic properties of real-world
    3D objects is crucial for the continued evolution of augmented reality and robotic
    applications. To this end, we present \algfull{} (\algname{}), which incorporates
    vision-language embeddings of foundation models into 3D Gaussian Splatting (GS).
    The key contribution of this work is an efficient method to reconstruct and represent
    3D vision-language models. This is achieved by distilling feature maps generated
    from image-based foundation models into those rendered from our 3D model. To ensure
    high-quality rendering and fast training, we introduce a novel scene representation
    by integrating strengths from both GS and multi-resolution hash encodings (MHE).
    Our effective training procedure also introduces a pixel alignment loss that makes
    the rendered feature distance of same semantic entities close, following the pixel-level
    semantic boundaries. Our results demonstrate remarkable multi-view semantic consistency,
    facilitating diverse downstream tasks, beating state-of-the-art methods by 10.2
    percent on open-vocabulary language-based object detection, despite that we are
    851× faster for inference. This research explores the intersection of vision,
    language, and 3D scene representation, paving the way for enhanced scene understanding
    in uncontrolled real-world environments.
  project_page: https://xingxingzuo.github.io/fmgs/
  paper: https://arxiv.org/pdf/2401.01970.pdf
  code: https://github.com/google-research/foundation-model-embedded-3dgs
  video: null
  tags:
  - Code
  - Language Embedding
  - Project
  - Segmentation
  thumbnail: assets/thumbnails/zuo2024fmgs.jpg
  publication_date: '2024-01-03T20:39:02+00:00'
- id: yan2024street
  title: Street Gaussians for Modeling Dynamic Urban Scenes
  authors: Yunzhi Yan, Haotong Lin, Chenxu Zhou, Weijie Wang, Haiyang Sun, Kun Zhan,
    Xianpeng Lang, Xiaowei Zhou, Sida Peng
  year: '2024'
  abstract: This paper aims to tackle the problem of modeling dynamic urban street
    scenes from monocular videos. Recent methods extend NeRF by incorporating tracked
    vehicle poses to animate vehicles, enabling photo-realistic view synthesis of
    dynamic urban street scenes. However, significant limitations are their slow training
    and rendering speed, coupled with the critical need for high precision in tracked
    vehicle poses. We introduce Street Gaussians, a new explicit scene representation
    that tackles all these limitations. Specifically, the dynamic urban street is
    represented as a set of point clouds equipped with semantic logits and 3D Gaussians,
    each associated with either a foreground vehicle or the background. To model the
    dynamics of foreground object vehicles, each object point cloud is optimized with
    optimizable tracked poses, along with a dynamic spherical harmonics model for
    the dynamic appearance. The explicit representation allows easy composition of
    object vehicles and background, which in turn allows for scene editing operations
    and rendering at 133 FPS (1066×1600 resolution) within half an hour of training.
    The proposed method is evaluated on multiple challenging benchmarks, including
    KITTI and Waymo Open datasets. Experiments show that the proposed method consistently
    outperforms state-of-the-art methods across all datasets. Furthermore, the proposed
    representation delivers performance on par with that achieved using precise ground-truth
    poses, despite relying only on poses from an off-the-shelf tracker.
  project_page: https://zju3dv.github.io/street_gaussians/
  paper: https://arxiv.org/pdf/2401.01339.pdf
  code: https://github.com/zju3dv/street_gaussians
  video: null
  tags:
  - Autonomous Driving
  - Code
  - Project
  thumbnail: assets/thumbnails/yan2024street.jpg
  publication_date: '2024-01-02T18:59:55+00:00'
- id: lee2023deblurring
  title: Deblurring 3D Gaussian Splatting
  authors: Byeonghyeon Lee, Howoong Lee, Xiangyu Sun, Usman Ali, Eunbyung Park
  year: '2023'
  abstract: Recent studies in Radiance Fields have paved the robust way for novel
    view synthesis with their photorealistic rendering quality. Nevertheless, they
    usually employ neural networks and volumetric rendering, which are costly to train
    and impede their broad use in various real-time applications due to the lengthy
    rendering time. Lately 3D Gaussians splatting-based approach has been proposed
    to model the 3D scene, and it achieves remarkable visual quality while rendering
    the images in real-time. However, it suffers from severe degradation in the rendering
    quality if the training images are blurry. Blurriness commonly occurs due to the
    lens defocusing, object motion, and camera shake, and it inevitably intervenes
    in clean image acquisition. Several previous studies have attempted to render
    clean and sharp images from blurry input images using neural fields. The majority
    of those works, however, are designed only for volumetric rendering-based neural
    radiance fields and are not straightforwardly applicable to rasterization-based
    3D Gaussian splatting methods. Thus, we propose a novel real-time deblurring framework,
    deblurring 3D Gaussian Splatting, using a small Multi-Layer Perceptron (MLP) that
    manipulates the covariance of each 3D Gaussian to model the scene blurriness.
    While deblurring 3D Gaussian Splatting can still enjoy real-time rendering, it
    can reconstruct fine and sharp details from blurry images. A variety of experiments
    have been conducted on the benchmark, and the results have revealed the effectiveness
    of our approach for deblurring.
  project_page: https://benhenryl.github.io/Deblurring-3D-Gaussian-Splatting/
  paper: https://arxiv.org/pdf/2401.00834.pdf
  code: https://github.com/benhenryL/Deblurring-3D-Gaussian-Splatting
  video: null
  tags:
  - Code
  - Deblurring
  - Project
  thumbnail: assets/thumbnails/lee2023deblurring.jpg
  publication_date: '2024-01-01T18:23:51+00:00'
- id: yin20234dgen
  title: '4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency'
  authors: Yuyang Yin, Dejia Xu, Zhangyang Wang, Yao Zhao, Yunchao Wei
  year: '2023'
  abstract: Aided by text-to-image and text-to-video diffusion models, existing 4D
    content creation pipelines utilize score distillation sampling to optimize entire
    dynamic 3D scene. However, as these pipelines generate 4D content from text or
    image inputs, they incur significant time and effort in prompt engineering through
    trial and error. This work introduces 4DGen, a novel, holistic framework for grounded
    4D content generation that decomposes the 4D generation task into multiple stages.
    We identify static 3D assets and monocular video sequences as key components in
    constructing the 4D content. Our pipeline facilitates conditional 4D generation,
    enabling users to specify geometry (3D assets) and motion (monocular videos),
    thus offering superior control over content creation. Furthermore, we construct
    our 4D representation using dynamic 3D Gaussians, which permits efficient, high-resolution
    supervision through rendering during training, thereby facilitating high-quality
    4D generation. Additionally, we employ spatial-temporal pseudo labels on anchor
    frames, along with seamless consistency priors implemented through 3D-aware score
    distillation sampling and smoothness regularizations. Compared to existing baselines,
    our approach yields competitive results in faithfully reconstructing input signals
    and realistically inferring renderings from novel viewpoints and timesteps. Most
    importantly, our method supports grounded generation, offering users enhanced
    control, a feature difficult to achieve with previous methods.
  project_page: https://vita-group.github.io/4DGen/
  paper: https://arxiv.org/pdf/2312.17225.pdf
  code: https://github.com/VITA-Group/4DGen
  video: https://www.youtube.com/watch?v=-bXyBKdpQ1o
  tags:
  - Code
  - Diffusion
  - Project
  - Video
  thumbnail: assets/thumbnails/yin20234dgen.jpg
  publication_date: '2023-12-28T18:53:39+00:00'
- id: ren2023dreamgaussian4d
  title: 'DreamGaussian4D: Generative 4D Gaussian Splatting'
  authors: Jiawei Ren, Liang Pan, Jiaxiang Tang, Chi Zhang, Ang Cao, Gang Zeng, Ziwei
    Liu
  year: '2023'
  abstract: Remarkable progress has been made in 4D content generation recently. However,
    existing methods suffer from long optimization time, lack of motion controllability,
    and a low level of detail. In this paper, we introduce DreamGaussian4D, an efficient
    4D generation framework that builds on 4D Gaussian Splatting representation. Our
    key insight is that the explicit modeling of spatial transformations in Gaussian
    Splatting makes it more suitable for the 4D generation setting compared with implicit
    representations. DreamGaussian4D reduces the optimization time from several hours
    to just a few minutes, allows flexible control of the generated 3D motion, and
    produces animated meshes that can be efficiently rendered in 3D engines.
  project_page: https://jiawei-ren.github.io/projects/dreamgaussian4d/
  paper: https://arxiv.org/pdf/2312.17142.pdf
  code: https://github.com/jiawei-ren/dreamgaussian4d
  video: null
  tags:
  - Code
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/ren2023dreamgaussian4d.jpg
  publication_date: '2023-12-28T17:16:44+00:00'
- id: li2023spacetime
  title: Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis
  authors: Zhan Li, Zhang Chen, Zhong Li, Yi Xu
  year: '2023'
  abstract: Novel view synthesis of dynamic scenes has been an intriguing yet challenging
    problem. Despite recent advancements, simultaneously achieving high-resolution
    photorealistic results, real-time rendering, and compact storage remains a formidable
    task. To address these challenges, we propose Spacetime Gaussian Feature Splatting
    as a novel dynamic scene representation, composed of three pivotal components.
    First, we formulate expressive Spacetime Gaussians by enhancing 3D Gaussians with
    temporal opacity and parametric motion/rotation. This enables Spacetime Gaussians
    to capture static, dynamic, as well as transient content within a scene. Second,
    we introduce splatted feature rendering, which replaces spherical harmonics with
    neural features. These features facilitate the modeling of view- and time-dependent
    appearance while maintaining small size. Third, we leverage the guidance of training
    error and coarse depth to sample new Gaussians in areas that are challenging to
    converge with existing pipelines. Experiments on several established real-world
    datasets demonstrate that our method achieves state-of-the-art rendering quality
    and speed, while retaining compact storage. At 8K resolution, our lite-version
    model can render at 60 FPS on an Nvidia RTX 4090 GPU.
  project_page: https://oppo-us-research.github.io/SpacetimeGaussians-website/
  paper: https://arxiv.org/pdf/2312.16812.pdf
  code: https://github.com/oppo-us-research/SpacetimeGaussians
  video: https://www.youtube.com/watch?v=YsPPmf-E6Lg
  tags:
  - Code
  - Dynamic
  - Project
  - Video
  thumbnail: assets/thumbnails/li2023spacetime.jpg
  publication_date: '2023-12-28T04:14:55+00:00'
- id: qin2024langsplat
  title: 'LangSplat: 3D Language Gaussian Splatting'
  authors: Minghan Qin, Wanhua Li, Jiawei Zhou, Haoqian Wang, Hanspeter Pfister
  year: '2024'
  abstract: Human lives in a 3D world and commonly uses natural language to interact
    with a 3D scene. Modeling a 3D language field to support open-ended language queries
    in 3D has gained increasing attention recently. This paper introduces LangSplat,
    which constructs a 3D language field that enables precise and efficient open-vocabulary
    querying within 3D spaces. Unlike existing methods that ground CLIP language embeddings
    in a NeRF model, LangSplat advances the field by utilizing a collection of 3D
    Gaussians, each encoding language features distilled from CLIP, to represent the
    language field. By employing a tile-based splatting technique for rendering language
    features, we circumvent the costly rendering process inherent in NeRF. Instead
    of directly learning CLIP embeddings, LangSplat first trains a scene-wise language
    autoencoder and then learns language features on the scene-specific latent space,
    thereby alleviating substantial memory demands imposed by explicit modeling. Existing
    methods struggle with imprecise and vague 3D language fields, which fail to discern
    clear boundaries between objects. We delve into this issue and propose to learn
    hierarchical semantics using SAM, thereby eliminating the need for extensively
    querying the language field across various scales and the regularization of DINO
    features. Extensive experiments on open-vocabulary 3D object localization and
    semantic segmentation demonstrate that LangSplat significantly outperforms the
    previous state-of-the-art method LERF by a large margin. Notably, LangSplat is
    extremely efficient, achieving a {\speed} × speedup compared to LERF at the resolution
    of 1440 × 1080.
  project_page: https://langsplat.github.io/
  paper: https://arxiv.org/pdf/2312.16084.pdf
  code: https://github.com/minghanqin/LangSplat
  video: https://www.youtube.com/watch?v=XMlyjsei-Es
  tags:
  - Code
  - Language Embedding
  - Project
  - Segmentation
  - Video
  thumbnail: assets/thumbnails/qin2024langsplat.jpg
  publication_date: '2023-12-26T15:14:37+00:00'
- id: lan20232dguided
  title: 2D-Guided 3D Gaussian Segmentation
  authors: Kun Lan, Haoran Li, Haolin Shi, Wenjun Wu, Yong Liao, Lin Wang, Pengyuan
    Zhou
  year: '2023'
  abstract: Recently, 3D Gaussian, as an explicit 3D representation method, has demonstrated
    strong competitiveness over NeRF (Neural Radiance Fields) in terms of expressing
    complex scenes and training duration. These advantages signal a wide range of
    applications for 3D Gaussians in 3D understanding and editing. Meanwhile, the
    segmentation of 3D Gaussians is still in its infancy. The existing segmentation
    methods are not only cumbersome but also incapable of segmenting multiple objects
    simultaneously in a short amount of time. In response, this paper introduces a
    3D Gaussian segmentation method implemented with 2D segmentation as supervision.
    This approach uses input 2D segmentation maps to guide the learning of the added
    3D Gaussian semantic information, while nearest neighbor clustering and statistical
    filtering refine the segmentation results. Experiments show that our concise method
    can achieve comparable performances on mIOU and mAcc for multi-object segmentation
    as previous single-object segmentation methods.
  project_page: null
  paper: https://arxiv.org/pdf/2312.16047.pdf
  code: null
  video: null
  tags:
  - Segmentation
  thumbnail: assets/thumbnails/lan20232dguided.jpg
  publication_date: '2023-12-26T13:28:21+00:00'
- id: li2023human101
  title: 'Human101: Training 100+FPS Human Gaussians in 100s from 1 View'
  authors: Mingwei Li, Jiachen Tao, Zongxin Yang, Yi Yang
  year: '2023'
  abstract: Reconstructing the human body from single-view videos plays a pivotal
    role in the virtual reality domain. One prevalent application scenario necessitates
    the rapid reconstruction of high-fidelity 3D digital humans while simultaneously
    ensuring real-time rendering and interaction. Existing methods often struggle
    to fulfill both requirements. In this paper, we introduce Human101, a novel framework
    adept at producing high-fidelity dynamic 3D human reconstructions from 1-view
    videos by training 3D Gaussians in 100 seconds and rendering in 100+ FPS. Our
    method leverages the strengths of 3D Gaussian Splatting, which provides an explicit
    and efficient representation of 3D humans. Standing apart from prior NeRF-based
    pipelines, Human101 ingeniously applies a Human-centric Forward Gaussian Animation
    method to deform the parameters of 3D Gaussians, thereby enhancing rendering speed
    (i.e., rendering 1024-resolution images at an impressive 60+ FPS and rendering
    512-resolution images at 100+ FPS). Experimental results indicate that our approach
    substantially eclipses current methods, clocking up to a 10 times surge in frames
    per second and delivering comparable or superior rendering quality.
  project_page: https://longxiang-ai.github.io/Human101/
  paper: https://arxiv.org/pdf/2312.15258.pdf
  code: https://github.com/longxiang-ai/Human101
  video: null
  tags:
  - Avatar
  - Code
  - Project
  thumbnail: assets/thumbnails/li2023human101.jpg
  publication_date: '2023-12-23T13:41:56+00:00'
- id: jung2023deformable
  title: Deformable 3D Gaussian Splatting for Animatable Human Avatars
  authors: HyunJun Jung, Nikolas Brasch, Jifei Song, Eduardo Perez-Pellitero, Yiren
    Zhou, Zhihao Li, Nassir Navab, Benjamin Busam
  year: '2023'
  abstract: 'Recent advances in neural radiance fields enable novel view synthesis
    of photo-realistic images in dynamic settings, which can be applied to scenarios
    with human animation. Commonly used implicit backbones to establish accurate models,
    however, require many input views and additional annotations such as human masks,
    UV maps and depth maps. In this work, we propose ParDy-Human (Parameterized Dynamic
    Human Avatar), a fully explicit approach to construct a digital avatar from as
    little as a single monocular sequence. ParDy-Human introduces parameter-driven
    dynamics into 3D Gaussian Splatting where 3D Gaussians are deformed by a human
    pose model to animate the avatar. Our method is composed of two parts: A first
    module that deforms canonical 3D Gaussians according to SMPL vertices and a consecutive
    module that further takes their designed joint encodings and predicts per Gaussian
    deformations to deal with dynamics beyond SMPL vertex deformations. Images are
    then synthesized by a rasterizer. ParDy-Human constitutes an explicit model for
    realistic dynamic human avatars which requires significantly fewer training views
    and images. Our avatars learning is free of additional annotations such as masks
    and can be trained with variable backgrounds while inferring full-resolution images
    efficiently even on consumer hardware. We provide experimental evidence to show
    that ParDy-Human outperforms state-of-the-art methods on ZJU-MoCap and THUman4.0
    datasets both quantitatively and visually.'
  project_page: null
  paper: https://arxiv.org/pdf/2312.15059.pdf
  code: null
  video: null
  tags:
  - Avatar
  thumbnail: assets/thumbnails/jung2023deformable.jpg
  publication_date: '2023-12-22T20:56:46+00:00'
- id: wang2023dust3r
  title: 'DUSt3R: Geometric 3D Vision Made Easy'
  authors: Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, Jerome Revaud
  year: '2023'
  abstract: 'Multi-view stereo reconstruction (MVS) in the wild requires to first
    estimate the camera parameters e.g. intrinsic and extrinsic parameters. These
    are usually tedious and cumbersome to obtain, yet they are mandatory to triangulate
    corresponding pixels in 3D space, which is the core of all best performing MVS
    algorithms. In this work, we take an opposite stance and introduce DUSt3R, a radically
    novel paradigm for Dense and Unconstrained Stereo 3D Reconstruction of arbitrary
    image collections, i.e. operating without prior information about camera calibration
    nor viewpoint poses. We cast the pairwise reconstruction problem as a regression
    of pointmaps, relaxing the hard constraints of usual projective camera models.
    We show that this formulation smoothly unifies the monocular and binocular reconstruction
    cases. In the case where more than two images are provided, we further propose
    a simple yet effective global alignment strategy that expresses all pairwise pointmaps
    in a common reference frame. We base our network architecture on standard Transformer
    encoders and decoders, allowing us to leverage powerful pretrained models. Our
    formulation directly provides a 3D model of the scene as well as depth information,
    but interestingly, we can seamlessly recover from it, pixel matches, relative
    and absolute camera. Exhaustive experiments on all these tasks showcase that the
    proposed DUSt3R can unify various 3D vision tasks and set new SoTAs on monocular/multi-view
    depth estimation as well as relative pose estimation. In summary, DUSt3R makes
    many geometric 3D vision tasks easy.

    '
  project_page: https://europe.naverlabs.com/research/publications/dust3r-geometric-3d-vision-made-easy/
  paper: https://arxiv.org/pdf/2312.14132.pdf
  code: https://github.com/naver/dust3r
  video: https://europe.naverlabs.com/wp-content/uploads/2024/09/supmat_dust3r_final_light_h264.mp4?_=1
  tags:
  - 3ster-based
  - Code
  - Project
  - Video
  thumbnail: assets/thumbnails/wang2023dust3r.jpg
  publication_date: '2023-12-21T18:52:14+00:00'
  date_source: arxiv
- id: malarz2023gaussian
  title: Gaussian Splatting with NeRF-based Color and Opacity
  authors: Dawid Malarz, Weronika Smolak, Jacek Tabor, Sławomir Tadeja, Przemysław
    Spurek
  year: '2023'
  abstract: Neural Radiance Fields (NeRFs) have demonstrated the remarkable potential
    of neural networks to capture the intricacies of 3D objects. By encoding the shape
    and color information within neural network weights, NeRFs excel at producing
    strikingly sharp novel views of 3D objects. Recently, numerous generalizations
    of NeRFs utilizing generative models have emerged, expanding its versatility.
    In contrast, Gaussian Splatting (GS) offers a similar renders quality with faster
    training and inference as it does not need neural networks to work. We encode
    information about the 3D objects in the set of Gaussian distributions that can
    be rendered in 3D similarly to classical meshes. Unfortunately, GS are difficult
    to condition since they usually require circa hundred thousand Gaussian components.
    To mitigate the caveats of both models, we propose a hybrid model that uses GS
    representation of the 3D object's shape and NeRF-based encoding of color and opacity.
    Our model uses Gaussian distributions with trainable positions (i.e. means of
    Gaussian), shape (i.e. covariance of Gaussian), color and opacity, and neural
    network, which takes parameters of Gaussian and viewing direction to produce changes
    in color and opacity. Consequently, our model better describes shadows, light
    reflections, and transparency of 3D objects.
  project_page: null
  paper: https://arxiv.org/pdf/2312.13729.pdf
  code: https://github.com/gmum/ViewingDirectionGaussianSplatting
  video: null
  tags:
  - Code
  - Rendering
  thumbnail: assets/thumbnails/malarz2023gaussian.jpg
  publication_date: '2023-12-21T10:52:59+00:00'
- id: zhang2023repaint123
  title: 'Repaint123: Fast and High-quality One Image to 3D Generation with Progressive
    Controllable 2D Repainting'
  authors: Junwu Zhang, Zhenyu Tang, Yatian Pang, Xinhua Cheng, Peng Jin, Yida Wei,
    Munan Ning, Li Yuan
  year: '2023'
  abstract: Recent one image to 3D generation methods commonly adopt Score Distillation
    Sampling (SDS). Despite the impressive results, there are multiple deficiencies
    including multi-view inconsistency, over-saturated and over-smoothed textures,
    as well as the slow generation speed. To address these deficiencies, we present
    Repaint123 to alleviate multi-view bias as well as texture degradation and speed
    up the generation process. The core idea is to combine the powerful image generation
    capability of the 2D diffusion model and the texture alignment ability of the
    repainting strategy for generating high-quality multi-view images with consistency.
    We further propose visibility-aware adaptive repainting strength for overlap regions
    to enhance the generated image quality in the repainting process. The generated
    high-quality and multi-view consistent images enable the use of simple Mean Square
    Error (MSE) loss for fast 3D content generation. We conduct extensive experiments
    and show that our method has a superior ability to generate high-quality 3D content
    with multi-view consistency and fine textures in 2 minutes from scratch.
  project_page: https://pku-yuangroup.github.io/repaint123/
  paper: https://arxiv.org/pdf/2312.13271.pdf
  code: null
  video: null
  tags:
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/zhang2023repaint123.jpg
  publication_date: '2023-12-20T18:51:02+00:00'
- id: szymanowicz2023splatter
  title: 'Splatter Image: Ultra-Fast Single-View 3D Reconstruction'
  authors: Stanislaw Szymanowicz, Christian Rupprecht, Andrea Vedaldi
  year: '2023'
  abstract: 'We introduce the Splatter Image, an ultra-fast approach for monocular
    3D object reconstruction which operates at 38 FPS. Splatter Image is based on
    Gaussian Splatting, which has recently brought real-time rendering, fast training,
    and excellent scaling to multi-view reconstruction. For the first time, we apply
    Gaussian Splatting in a monocular reconstruction setting. Our approach is learning-based,
    and, at test time, reconstruction only requires the feed-forward evaluation of
    a neural network. The main innovation of Splatter Image is the surprisingly straightforward
    design: it uses a 2D image-to-image network to map the input image to one 3D Gaussian
    per pixel. The resulting Gaussians thus have the form of an image, the Splatter
    Image. We further extend the method to incorporate more than one image as input,
    which we do by adding cross-view attention. Owning to the speed of the renderer
    (588 FPS), we can use a single GPU for training while generating entire images
    at each iteration in order to optimize perceptual metrics like LPIPS. On standard
    benchmarks, we demonstrate not only fast reconstruction but also better results
    than recent and much more expensive baselines in terms of PSNR, LPIPS, and other
    metrics.'
  project_page: https://szymanowiczs.github.io/splatter-image.html
  paper: https://arxiv.org/pdf/2312.13150.pdf
  code: https://github.com/szymanowiczs/splatter-image
  video: https://www.youtube.com/watch?v=pcKTf9SVh4g
  tags:
  - Code
  - Feed-Forward
  - Project
  - Sparse
  - Video
  thumbnail: assets/thumbnails/szymanowicz2023splatter.jpg
  publication_date: '2023-12-20T16:14:58+00:00'
- id: shaw2023swings
  title: 'SWinGS: Sliding Windows for Dynamic 3D Gaussian Splatting'
  authors: Richard Shaw, Michal Nazarczuk, Jifei Song, Arthur Moreau, Sibi Catley-Chandar,
    Helisa Dhamo, Eduardo Perez-Pellitero
  year: '2023'
  abstract: Novel view synthesis has shown rapid progress recently, with methods capable
    of producing increasingly photorealistic results. 3D Gaussian Splatting has emerged
    as a promising method, producing high-quality renderings of scenes and enabling
    interactive viewing at real-time frame rates. However, it is limited to static
    scenes. In this work, we extend 3D Gaussian Splatting to reconstruct dynamic scenes.
    We model a scene's dynamics using dynamic MLPs, learning deformations from temporally-local
    canonical representations to per-frame 3D Gaussians. To disentangle static and
    dynamic regions, tuneable parameters weigh each Gaussian's respective MLP parameters,
    improving the dynamics modelling of imbalanced scenes. We introduce a sliding
    window training strategy that partitions the sequence into smaller manageable
    windows to handle arbitrary length scenes while maintaining high rendering quality.
    We propose an adaptive sampling strategy to determine appropriate window size
    hyperparameters based on the scene's motion, balancing training overhead with
    visual quality. Training a separate dynamic 3D Gaussian model for each sliding
    window allows the canonical representation to change, enabling the reconstruction
    of scenes with significant geometric changes. Temporal consistency is enforced
    using a fine-tuning step with self-supervising consistency loss on randomly sampled
    novel views. As a result, our method produces high-quality renderings of general
    dynamic scenes with competitive quantitative performance, which can be viewed
    in real-time in our dynamic interactive viewer.
  project_page: null
  paper: https://arxiv.org/pdf/2312.13308.pdf
  code: null
  video: null
  tags:
  - Dynamic
  thumbnail: assets/thumbnails/shaw2023swings.jpg
  publication_date: '2023-12-20T03:54:03+00:00'
- id: morgenstern2023compact
  title: Compact 3D Scene Representation via Self-Organizing Gaussian Grids
  authors: Wieland Morgenstern, Florian Barthel, Anna Hilsmann, Peter Eisert
  year: '2023'
  abstract: 3D Gaussian Splatting has recently emerged as a highly promising technique
    for modeling of static 3D scenes. In contrast to Neural Radiance Fields, it utilizes
    efficient rasterization allowing for very fast rendering at high-quality. However,
    the storage size is significantly higher, which hinders practical deployment,
    e.g. on resource constrained devices. In this paper, we introduce a compact scene
    representation organizing the parameters of 3D Gaussian Splatting (3DGS) into
    a 2D grid with local homogeneity, ensuring a drastic reduction in storage requirements
    without compromising visual quality during rendering. Central to our idea is the
    explicit exploitation of perceptual redundancies present in natural scenes. In
    essence, the inherent nature of a scene allows for numerous permutations of Gaussian
    parameters to equivalently represent it. To this end, we propose a novel highly
    parallel algorithm that regularly arranges the high-dimensional Gaussian parameters
    into a 2D grid while preserving their neighborhood structure. During training,
    we further enforce local smoothness between the sorted parameters in the grid.
    The uncompressed Gaussians use the same structure as 3DGS, ensuring a seamless
    integration with established renderers. Our method achieves a reduction factor
    of 17x to 42x in size for complex scenes with no increase in training time, marking
    a substantial leap forward in the domain of 3D scene distribution and consumption.
  project_page: https://fraunhoferhhi.github.io/Self-Organizing-Gaussians/
  paper: https://arxiv.org/pdf/2312.13299.pdf
  code: https://github.com/fraunhoferhhi/Self-Organizing-Gaussians
  video: https://www.youtube.com/watch?v=nb5U9xfx7-w
  tags:
  - Code
  - Compression
  - Project
  - Video
  thumbnail: assets/thumbnails/morgenstern2023compact.jpg
  publication_date: '2023-12-19T20:18:29+00:00'
- id: charatan2023pixelsplat
  title: 'pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable
    3D Reconstruction'
  authors: David Charatan, Sizhe Li, Andrea Tagliasacchi, Vincent Sitzmann
  year: '2023'
  abstract: We introduce pixelSplat, a feed-forward model that learns to reconstruct
    3D radiance fields parameterized by 3D Gaussian primitives from pairs of images.
    Our model features real-time and memory-efficient rendering for scalable training
    as well as fast 3D reconstruction at inference time. To overcome local minima
    inherent to sparse and locally supported representations, we predict a dense probability
    distribution over 3D and sample Gaussian means from that probability distribution.
    We make this sampling operation differentiable via a reparameterization trick,
    allowing us to back-propagate gradients through the Gaussian splatting representation.
    We benchmark our method on wide-baseline novel view synthesis on the real-world
    RealEstate10k and ACID datasets, where we outperform state-of-the-art light field
    transformers and accelerate rendering by 2.5 orders of magnitude while reconstructing
    an interpretable and editable 3D radiance field.
  project_page: https://davidcharatan.com/pixelsplat/
  paper: https://arxiv.org/pdf/2312.12337.pdf
  code: https://github.com/dcharatan/pixelsplat
  video: null
  tags:
  - Code
  - Feed-Forward
  - Project
  - Sparse
  thumbnail: assets/thumbnails/charatan2023pixelsplat.jpg
  publication_date: '2023-12-19T17:03:50+00:00'
- id: yuan2023gavatar
  title: 'GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning'
  authors: Ye Yuan, Xueting Li, Yangyi Huang, Shalini De Mello, Koki Nagano, Jan Kautz,
    Umar Iqbal
  year: '2023'
  abstract: Gaussian splatting has emerged as a powerful 3D representation that harnesses
    the advantages of both explicit (mesh) and implicit (NeRF) 3D representations.
    In this paper, we seek to leverage Gaussian splatting to generate realistic animatable
    avatars from textual descriptions, addressing the limitations (e.g., flexibility
    and efficiency) imposed by mesh or NeRF-based representations. However, a naive
    application of Gaussian splatting cannot generate high-quality animatable avatars
    and suffers from learning instability; it also cannot capture fine avatar geometries
    and often leads to degenerate body parts. To tackle these problems, we first propose
    a primitive-based 3D Gaussian representation where Gaussians are defined inside
    pose-driven primitives to facilitate animation. Second, to stabilize and amortize
    the learning of millions of Gaussians, we propose to use neural implicit fields
    to predict the Gaussian attributes (e.g., colors). Finally, to capture fine avatar
    geometries and extract detailed meshes, we propose a novel SDF-based implicit
    mesh learning approach for 3D Gaussians that regularizes the underlying geometries
    and extracts highly detailed textured meshes. Our proposed method, GAvatar, enables
    the large-scale generation of diverse animatable avatars using only text prompts.
    GAvatar significantly surpasses existing methods in terms of both appearance and
    geometry quality, and achieves extremely fast rendering (100 fps) at 1K resolution.
  project_page: https://nvlabs.github.io/GAvatar/
  paper: https://arxiv.org/pdf/2312.11461.pdf
  code: null
  video: https://www.youtube.com/watch?v=PbCF1HzrKrs
  tags:
  - Avatar
  - Meshing
  - Project
  - Video
  thumbnail: assets/thumbnails/yuan2023gavatar.jpg
  publication_date: '2023-12-18T18:59:12+00:00'
- id: liang2023gaufre
  title: 'GauFRe: Gaussian Deformation Fields for Real-time Dynamic Novel View Synthesis'
  authors: Yiqing Liang, Numair Khan, Zhengqin Li, Thu Nguyen-Phuoc, Douglas Lanman,
    James Tompkin, Lei Xiao
  year: '2023'
  abstract: We propose a method for dynamic scene reconstruction using deformable
    3D Gaussians that is tailored for monocular video. Building upon the efficiency
    of Gaussian splatting, our approach extends the representation to accommodate
    dynamic elements via a deformable set of Gaussians residing in a canonical space,
    and a time-dependent deformation field defined by a multi-layer perceptron (MLP).
    Moreover, under the assumption that most natural scenes have large regions that
    remain static, we allow the MLP to focus its representational power by additionally
    including a static Gaussian point cloud. The concatenated dynamic and static point
    clouds form the input for the Gaussian Splatting rasterizer, enabling real-time
    rendering. The differentiable pipeline is optimized end-to-end with a self-supervised
    rendering loss. Our method achieves results that are comparable to state-of-the-art
    dynamic neural radiance field methods while allowing much faster optimization
    and rendering.
  project_page: https://lynl7130.github.io/gaufre/index.html
  paper: https://arxiv.org/pdf/2312.11458.pdf
  code: null
  video: https://youtu.be/YweWidWO8rI?si=jMssQdIXQV67kwzS
  tags:
  - Dynamic
  - Project
  - Video
  thumbnail: assets/thumbnails/liang2023gaufre.jpg
  publication_date: '2023-12-18T18:59:03+00:00'
- id: ouyang2023text2immersion
  title: 'Text2Immersion: Generative Immersive Scene with 3D Gaussian'
  authors: Hao Ouyang, Kathryn Heal, Stephen Lombardi, Tiancheng Sun
  year: '2023'
  abstract: We introduce Text2Immersion, an elegant method for producing high-quality
    3D immersive scenes from text prompts. Our proposed pipeline initiates by progressively
    generating a Gaussian cloud using pre-trained 2D diffusion and depth estimation
    models. This is followed by a refining stage on the Gaussian cloud, interpolating
    and refining it to enhance the details of the generated scene. Distinct from prevalent
    methods that focus on single object or indoor scenes, or employ zoom-out trajectories,
    our approach generates diverse scenes with various objects, even extending to
    the creation of imaginary scenes. Consequently, Text2Immersion can have wide-ranging
    implications for various applications such as virtual reality, game development,
    and automated content creation. Extensive evaluations demonstrate that our system
    surpasses other methods in rendering quality and diversity, further progressing
    towards text-driven 3D scene generation.
  project_page: https://ken-ouyang.github.io/text2immersion/index.html
  paper: https://arxiv.org/pdf/2312.09242.pdf
  code: null
  video: null
  tags:
  - Diffusion
  - Project
  - World Generation
  thumbnail: assets/thumbnails/ouyang2023text2immersion.jpg
  publication_date: '2023-12-14T18:58:47+00:00'
- id: qian20233dgsavatar
  title: '3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting'
  authors: Zhiyin Qian, Shaofei Wang, Marko Mihajlovic, Andreas Geiger, Siyu Tang
  year: '2023'
  abstract: We introduce an approach that creates animatable human avatars from monocular
    videos using 3D Gaussian Splatting (3DGS). Existing methods based on neural radiance
    fields (NeRFs) achieve high-quality novel-view/novel-pose image synthesis but
    often require days of training, and are extremely slow at inference time. Recently,
    the community has explored fast grid structures for efficient training of clothed
    avatars. Albeit being extremely fast at training, these methods can barely achieve
    an interactive rendering frame rate with around 15 FPS. In this paper, we use
    3D Gaussian Splatting and learn a non-rigid deformation network to reconstruct
    animatable clothed human avatars that can be trained within 30 minutes and rendered
    at real-time frame rates (50+ FPS). Given the explicit nature of our representation,
    we further introduce as-isometric-as-possible regularizations on both the Gaussian
    mean vectors and the covariance matrices, enhancing the generalization of our
    model on highly articulated unseen poses. Experimental results show that our method
    achieves comparable and even better performance compared to state-of-the-art approaches
    on animatable avatar creation from a monocular input, while being 400x and 250x
    faster in training and inference, respectively.
  project_page: https://neuralbodies.github.io/3DGS-Avatar/index.html
  paper: https://arxiv.org/pdf/2312.09228.pdf
  code: https://github.com/mikeqzy/3dgs-avatar-release
  video: https://youtu.be/FJ29U9OkmmU?si=5ua2mtpv5ei2n28Z
  tags:
  - Avatar
  - Code
  - Project
  - Video
  thumbnail: assets/thumbnails/qian20233dgsavatar.jpg
  publication_date: '2023-12-14T18:54:32+00:00'
- id: zou2023triplane
  title: 'Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D
    Reconstruction with Transformers'
  authors: Zi-Xin Zou, Zhipeng Yu, Yuan-Chen Guo, Yangguang Li, Ding Liang, Yan-Pei
    Cao, Song-Hai Zhang
  year: '2023'
  abstract: Recent advancements in 3D reconstruction from single images have been
    driven by the evolution of generative models. Prominent among these are methods
    based on Score Distillation Sampling (SDS) and the adaptation of diffusion models
    in the 3D domain. Despite their progress, these techniques often face limitations
    due to slow optimization or rendering processes, leading to extensive training
    and optimization times. In this paper, we introduce a novel approach for single-view
    reconstruction that efficiently generates a 3D model from a single image via feed-forward
    inference. Our method utilizes two transformer-based networks, namely a point
    decoder and a triplane decoder, to reconstruct 3D objects using a hybrid Triplane-Gaussian
    intermediate representation. This hybrid representation strikes a balance, achieving
    a faster rendering speed compared to implicit representations while simultaneously
    delivering superior rendering quality than explicit representations. The point
    decoder is designed for generating point clouds from single images, offering an
    explicit representation which is then utilized by the triplane decoder to query
    Gaussian features for each point. This design choice addresses the challenges
    associated with directly regressing explicit 3D Gaussian attributes characterized
    by their non-structural nature. Subsequently, the 3D Gaussians are decoded by
    an MLP to enable rapid rendering through splatting. Both decoders are built upon
    a scalable, transformer-based architecture and have been efficiently trained on
    large-scale 3D datasets. The evaluations conducted on both synthetic datasets
    and real-world images demonstrate that our method not only achieves higher quality
    but also ensures a faster runtime in comparison to previous state-of-the-art techniques.
  project_page: https://zouzx.github.io/TriplaneGaussian/
  paper: https://arxiv.org/pdf/2312.09147.pdf
  code: https://github.com/VAST-AI-Research/TriplaneGaussian
  video: null
  tags:
  - Code
  - Misc
  - Project
  - Transformer
  thumbnail: assets/thumbnails/zou2023triplane.jpg
  publication_date: '2023-12-14T17:18:34+00:00'
- id: sun2023icomma
  title: 'iComMa: Inverting 3D Gaussians Splatting for Camera Pose Estimation via
    Comparing and Matching'
  authors: Yuan Sun, Xuan Wang, Yunfan Zhang, Jie Zhang, Caigui Jiang, Yu Guo, Fei
    Wang
  year: '2023'
  abstract: We present a method named iComMa to address the 6D pose estimation problem
    in computer vision. The conventional pose estimation methods typically rely on
    the target's CAD model or necessitate specific network training tailored to particular
    object classes. Some existing methods address mesh-free 6D pose estimation by
    employing the inversion of a Neural Radiance Field (NeRF), aiming to overcome
    the aforementioned constraints. However, it still suffers from adverse initializations.
    By contrast, we model the pose estimation as the problem of inverting the 3D Gaussian
    Splatting (3DGS) with both the comparing and matching loss. In detail, a render-and-compare
    strategy is adopted for the precise estimation of poses. Additionally, a matching
    module is designed to enhance the model's robustness against adverse initializations
    by minimizing the distances between 2D keypoints. This framework systematically
    incorporates the distinctive characteristics and inherent rationale of render-and-compare
    and matching-based approaches. This comprehensive consideration equips the framework
    to effectively address a broader range of intricate and challenging scenarios,
    including instances with substantial angular deviations, all while maintaining
    a high level of prediction accuracy. Experimental results demonstrate the superior
    precision and robustness of our proposed jointly optimized framework when evaluated
    on synthetic and complex real-world data in challenging scenarios.
  project_page: null
  paper: https://arxiv.org/pdf/2312.09031.pdf
  code: https://github.com/YuanSun-XJTU/iComMa
  video: null
  tags:
  - Poses
  thumbnail: assets/thumbnails/sun2023icomma.jpg
  publication_date: '2023-12-14T15:31:33+00:00'
- id: zhou2023drivinggaussian
  title: 'DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous
    Driving Scenes'
  authors: Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan, Yongtao Wang, Deqing Sun, Ming-Hsuan
    Yang
  year: '2023'
  abstract: We present DrivingGaussian, an efficient and effective framework for surrounding
    dynamic autonomous driving scenes. For complex scenes with moving objects, we
    first sequentially and progressively model the static background of the entire
    scene with incremental static 3D Gaussians. We then leverage a composite dynamic
    Gaussian graph to handle multiple moving objects, individually reconstructing
    each object and restoring their accurate positions and occlusion relationships
    within the scene. We further use a LiDAR prior for Gaussian Splatting to reconstruct
    scenes with greater details and maintain panoramic consistency. DrivingGaussian
    outperforms existing methods in driving scene reconstruction and enables photorealistic
    surround-view synthesis with high-fidelity and multi-camera consistency.
  project_page: https://pkuvdig.github.io/DrivingGaussian/
  paper: https://arxiv.org/pdf/2312.07920.pdf
  code: null
  video: null
  tags:
  - Autonomous Driving
  - Project
  thumbnail: assets/thumbnails/zhou2023drivinggaussian.jpg
  publication_date: '2023-12-13T06:30:51+00:00'
- id: fu2023colmapfree
  title: COLMAP-Free 3D Gaussian Splatting
  authors: Yang Fu, Sifei Liu, Amey Kulkarni, Jan Kautz, Alexei A. Efros, Xiaolong
    Wang
  year: '2023'
  abstract: While neural rendering has led to impressive advances in scene reconstruction
    and novel view synthesis, it relies heavily on accurately pre-computed camera
    poses. To relax this constraint, multiple efforts have been made to train Neural
    Radiance Fields (NeRFs) without pre-processed camera poses. However, the implicit
    representations of NeRFs provide extra challenges to optimize the 3D structure
    and camera poses at the same time. On the other hand, the recently proposed 3D
    Gaussian Splatting provides new opportunities given its explicit point cloud representations.
    This paper leverages both the explicit geometric representation and the continuity
    of the input video stream to perform novel view synthesis without any SfM preprocessing.
    We process the input frames in a sequential manner and progressively grow the
    3D Gaussians set by taking one input frame at a time, without the need to pre-compute
    the camera poses. Our method significantly improves over previous approaches in
    view synthesis and camera pose estimation under large motion changes.
  project_page: https://oasisyang.github.io/colmap-free-3dgs/
  paper: https://arxiv.org/pdf/2312.07504.pdf
  code: https://github.com/NVlabs/CF-3DGS
  video: https://youtu.be/IJtnx4keJvg
  tags:
  - Code
  - Poses
  - Project
  - Video
  thumbnail: assets/thumbnails/fu2023colmapfree.jpg
  publication_date: '2023-12-12T18:39:52+00:00'
- id: matsuki2023gaussian
  title: Gaussian Splatting SLAM
  authors: Hidenobu Matsuki, Riku Murai, Paul H. J. Kelly, Andrew J. Davison
  year: '2023'
  abstract: We present the first application of 3D Gaussian Splatting to incremental
    3D reconstruction using a single moving monocular or RGB-D camera. Our Simultaneous
    Localisation and Mapping (SLAM) method, which runs live at 3fps, utilises Gaussians
    as the only 3D representation, unifying the required representation for accurate,
    efficient tracking, mapping, and high-quality rendering. Several innovations are
    required to continuously reconstruct 3D scenes with high fidelity from a live
    camera. First, to move beyond the original 3DGS algorithm, which requires accurate
    poses from an offline Structure from Motion (SfM) system, we formulate camera
    tracking for 3DGS using direct optimisation against the 3D Gaussians, and show
    that this enables fast and robust tracking with a wide basin of convergence. Second,
    by utilising the explicit nature of the Gaussians, we introduce geometric verification
    and regularisation to handle the ambiguities occurring in incremental 3D dense
    reconstruction. Finally, we introduce a full SLAM system which not only achieves
    state-of-the-art results in novel view synthesis and trajectory estimation, but
    also reconstruction of tiny and even transparent objects.
  project_page: https://rmurai.co.uk/projects/GaussianSplattingSLAM/
  paper: https://arxiv.org/pdf/2312.06741.pdf
  code: https://github.com/muskie82/MonoGS
  video: https://youtu.be/x604ghp9R_Q?si=fPtz4kgBKFfcnQf3
  tags:
  - Code
  - Project
  - SLAM
  - Video
  thumbnail: assets/thumbnails/matsuki2023gaussian.jpg
  publication_date: '2023-12-11T18:19:04+00:00'
- id: pang2023ash
  title: 'ASH: Animatable Gaussian Splats for Efficient and Photoreal Human Rendering'
  authors: Haokai Pang, Heming Zhu, Adam Kortylewski, Christian Theobalt, Marc Habermann
  year: '2023'
  abstract: Real-time rendering of photorealistic and controllable human avatars stands
    as a cornerstone in Computer Vision and Graphics. While recent advances in neural
    implicit rendering have unlocked unprecedented photorealism for digital avatars,
    real-time performance has mostly been demonstrated for static scenes only. To
    address this, we propose ASH, an animatable Gaussian splatting approach for photorealistic
    rendering of dynamic humans in real-time. We parameterize the clothed human as
    animatable 3D Gaussians, which can be efficiently splatted into image space to
    generate the final rendering. However, naively learning the Gaussian parameters
    in 3D space poses a severe challenge in terms of compute. Instead, we attach the
    Gaussians onto a deformable character model, and learn their parameters in 2D
    texture space, which allows leveraging efficient 2D convolutional architectures
    that easily scale with the required number of Gaussians. We benchmark ASH with
    competing methods on pose-controllable avatars, demonstrating that our method
    outperforms existing real-time methods by a large margin and shows comparable
    or even better results than offline methods.
  project_page: https://vcai.mpi-inf.mpg.de/projects/ash/
  paper: https://arxiv.org/pdf/2312.05941.pdf
  code: https://github.com/kv2000/ASH
  video: https://vcai.mpi-inf.mpg.de/projects/ash/videos/video_for_page.mp4
  tags:
  - Avatar
  - Code
  - Project
  - Video
  thumbnail: assets/thumbnails/pang2023ash.jpg
  publication_date: '2023-12-10T17:07:37+00:00'
- id: yu2023cogs
  title: 'CoGS: Controllable Gaussian Splatting'
  authors: Heng Yu, Joel Julin, Zoltán Á. Milacski, Koichiro Niinuma, László A. Jeni
  year: '2023'
  abstract: Capturing and re-animating the 3D structure of articulated objects present
    significant barriers. On one hand, methods requiring extensively calibrated multi-view
    setups are prohibitively complex and resource-intensive, limiting their practical
    applicability. On the other hand, while single-camera Neural Radiance Fields (NeRFs)
    offer a more streamlined approach, they have excessive training and rendering
    costs. 3D Gaussian Splatting would be a suitable alternative but for two reasons.
    Firstly, existing methods for 3D dynamic Gaussians require synchronized multi-view
    cameras, and secondly, the lack of controllability in dynamic scenarios. We present
    CoGS, a method for Controllable Gaussian Splatting, that enables the direct manipulation
    of scene elements, offering real-time control of dynamic scenes without the prerequisite
    of pre-computing control signals. We evaluated CoGS using both synthetic and real-world
    datasets that include dynamic objects that differ in degree of difficulty. In
    our evaluations, CoGS consistently outperformed existing dynamic and controllable
    neural representations in terms of visual fidelity.
  project_page: https://cogs2023.github.io/
  paper: https://arxiv.org/pdf/2312.05664.pdf
  code: null
  video: null
  tags:
  - Dynamic
  - Project
  thumbnail: assets/thumbnails/yu2023cogs.jpg
  publication_date: '2023-12-09T20:06:29+00:00'
- id: shi2023gir
  title: 'GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization'
  authors: Yahao Shi, Yanmin Wu, Chenming Wu, Xing Liu, Chen Zhao, Haocheng Feng,
    Jingtuo Liu, Liangjun Zhang, Jian Zhang, Bin Zhou, Errui Ding, Jingdong Wang
  year: '2023'
  abstract: 'This paper presents GIR, a 3D Gaussian Inverse Rendering method for relightable
    scene factorization. Compared to existing methods leveraging discrete meshes or
    neural implicit fields for inverse rendering, our method utilizes 3D Gaussians
    to estimate the material properties, illumination, and geometry of an object from
    multi-view images. Our study is motivated by the evidence showing that 3D Gaussian
    is a more promising backbone than neural fields in terms of performance, versatility,
    and efficiency. In this paper, we aim to answer the question: "How can 3D Gaussian
    be applied to improve the performance of inverse rendering?" To address the complexity
    of estimating normals based on discrete and often in-homogeneous distributed 3D
    Gaussian representations, we proposed an efficient self-regularization method
    that facilitates the modeling of surface normals without the need for additional
    supervision. To reconstruct indirect illumination, we propose an approach that
    simulates ray tracing. Extensive experiments demonstrate our proposed GIR''s superior
    performance over existing methods across multiple tasks on a variety of widely
    used datasets in inverse rendering. This substantiates its efficacy and broad
    applicability, highlighting its potential as an influential tool in relighting
    and reconstruction.'
  project_page: https://3dgir.github.io/
  paper: https://arxiv.org/pdf/2312.05133
  code: null
  video: null
  tags:
  - Project
  - Relight
  - Rendering
  thumbnail: assets/thumbnails/shi2023gir.jpg
  publication_date: '2023-12-08T16:05:15+00:00'
- id: yang2023learn
  title: Learn to Optimize Denoising Scores for 3D Generation - A Unified and Improved
    Diffusion Prior on NeRF and 3D Gaussian Splatting
  authors: Xiaofeng Yang, Yiwen Chen, Cheng Chen, Chi Zhang, Yi Xu, Xulei Yang, Fayao
    Liu and Guosheng Lin
  year: '2023'
  abstract: We propose a unified framework aimed at enhancing the diffusion priors
    for 3D generation tasks. Despite the critical importance of these tasks, existing
    methodologies often struggle to generate high-caliber results. We begin by examining
    the inherent limitations in previous diffusion priors. We identify a divergence
    between the diffusion priors and the training procedures of diffusion models that
    substantially impairs the quality of 3D generation. To address this issue, we
    propose a novel, unified framework that iteratively optimizes both the 3D model
    and the diffusion prior. Leveraging the different learnable parameters of the
    diffusion prior, our approach offers multiple configurations, affording various
    trade-offs between performance and implementation complexity. Notably, our experimental
    results demonstrate that our method markedly surpasses existing techniques, establishing
    new state-of-the-art in the realm of text-to-3D generation. Furthermore, our approach
    exhibits impressive performance on both NeRF and the newly introduced 3D Gaussian
    Splatting backbones. Additionally, our framework yields insightful contributions
    to the understanding of recent score distillation methods, such as the VSD and
    DDS loss.
  project_page: https://yangxiaofeng.github.io/demo_diffusion_prior/
  paper: https://arxiv.org/pdf/2312.04820.pdf
  code: https://github.com/yangxiaofeng/LODS
  video: null
  tags:
  - Code
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/yang2023learn.jpg
  publication_date: '2023-12-08T03:55:34+00:00'
- id: girish2023eagles
  title: 'EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS'
  authors: Sharath Girish, Kamal Gupta, Abhinav Shrivastava
  year: '2023'
  abstract: Recently, 3D Gaussian splatting (3D-GS) has gained popularity in novel-view
    scene synthesis. It addresses the challenges of lengthy training times and slow
    rendering speeds associated with Neural Radiance Fields (NeRFs). Through rapid,
    differentiable rasterization of 3D Gaussians, 3D-GS achieves real-time rendering
    and accelerated training. They, however, demand substantial memory resources for
    both training and storage, as they require millions of Gaussians in their point
    cloud representation for each scene. We present a technique utilizing quantized
    embeddings to significantly reduce memory storage requirements and a coarse-to-fine
    training strategy for a faster and more stable optimization of the Gaussian point
    clouds. Our approach results in scene representations with fewer Gaussians and
    quantized representations, leading to faster training times and rendering speeds
    for real-time rendering of high resolution scenes. We reduce memory by more than
    an order of magnitude all while maintaining the reconstruction quality. We validate
    the effectiveness of our approach on a variety of datasets and scenes preserving
    the visual quality while consuming 10-20x less memory and faster training/inference
    speed.
  project_page: https://efficientgaussian.github.io/
  paper: https://arxiv.org/pdf/2312.04564.pdf
  code: https://github.com/Sharath-girish/efficientgaussian
  video: null
  tags:
  - Acceleration
  - Code
  - Densification
  - Project
  thumbnail: assets/thumbnails/girish2023eagles.jpg
  publication_date: '2023-12-07T18:59:55+00:00'
- id: chen2023monogaussianavatar
  title: 'MonoGaussianAvatar: Monocular Gaussian Point-based Head Avatar'
  authors: Yufan Chen, Lizhen Wang, Qijing Li, Hongjiang Xiao, Shengping Zhang, Hongxun
    Yao, Yebin Liu
  year: '2023'
  abstract: The ability to animate photo-realistic head avatars reconstructed from
    monocular portrait video sequences represents a crucial step in bridging the gap
    between the virtual and real worlds. Recent advancements in head avatar techniques,
    including explicit 3D morphable meshes (3DMM), point clouds, and neural implicit
    representation have been exploited for this ongoing research. However, 3DMM-based
    methods are constrained by their fixed topologies, point-based approaches suffer
    from a heavy training burden due to the extensive quantity of points involved,
    and the last ones suffer from limitations in deformation flexibility and rendering
    efficiency. In response to these challenges, we propose MonoGaussianAvatar (Monocular
    Gaussian Point-based Head Avatar), a novel approach that harnesses 3D Gaussian
    point representation coupled with a Gaussian deformation field to learn explicit
    head avatars from monocular portrait videos. We define our head avatars with Gaussian
    points characterized by adaptable shapes, enabling flexible topology. These points
    exhibit movement with a Gaussian deformation field in alignment with the target
    pose and expression of a person, facilitating efficient deformation. Additionally,
    the Gaussian points have controllable shape, size, color, and opacity combined
    with Gaussian splatting, allowing for efficient training and rendering. Experiments
    demonstrate the superior performance of our method, which achieves state-of-the-art
    results among previous methods.
  project_page: https://yufan1012.github.io/MonoGaussianAvatar
  paper: https://arxiv.org/pdf/2312.04558.pdf
  code: https://github.com/yufan1012/MonoGaussianAvatar
  video: https://youtu.be/3UvBkyPc-oc?si=SbveQKBLJh5GuhIY
  tags:
  - Avatar
  - Code
  - Monocular
  - Project
  - Video
  thumbnail: assets/thumbnails/chen2023monogaussianavatar.jpg
  publication_date: '2023-12-07T18:59:31+00:00'
- id: saito2023relightable
  title: Relightable Gaussian Codec Avatars
  authors: Shunsuke Saito, Gabriel Schwartz, Tomas Simon, Junxuan Li, Giljoo Nam
  year: '2023'
  abstract: The fidelity of relighting is bounded by both geometry and appearance
    representations. For geometry, both mesh and volumetric approaches have difficulty
    modeling intricate structures like 3D hair geometry. For appearance, existing
    relighting models are limited in fidelity and often too slow to render in real-time
    with high-resolution continuous environments. In this work, we present Relightable
    Gaussian Codec Avatars, a method to build high-fidelity relightable head avatars
    that can be animated to generate novel expressions. Our geometry model based on
    3D Gaussians can capture 3D-consistent sub-millimeter details such as hair strands
    and pores on dynamic face sequences. To support diverse materials of human heads
    such as the eyes, skin, and hair in a unified manner, we present a novel relightable
    appearance model based on learnable radiance transfer. Together with global illumination-aware
    spherical harmonics for the diffuse components, we achieve real-time relighting
    with spatially all-frequency reflections using spherical Gaussians. This appearance
    model can be efficiently relit under both point light and continuous illumination.
    We further improve the fidelity of eye reflections and enable explicit gaze control
    by introducing relightable explicit eye models. Our method outperforms existing
    approaches without compromising real-time performance. We also demonstrate real-time
    relighting of avatars on a tethered consumer VR headset, showcasing the efficiency
    and fidelity of our avatars.
  project_page: https://shunsukesaito.github.io/rgca/
  paper: https://arxiv.org/pdf/2312.03704.pdf
  code: https://github.com/facebookresearch/goliath
  video: https://shunsukesaito.github.io/rgca/static/videos/full_video.mp4
  tags:
  - Avatar
  - Code
  - Project
  - Relight
  - Video
  thumbnail: assets/thumbnails/saito2023relightable.jpg
  publication_date: '2023-12-06T18:59:58+00:00'
- id: jiang2023hifi4g
  title: 'HiFi4G: High-Fidelity Human Performance Rendering via Compact Gaussian Splatting'
  authors: Yuheng Jiang, Zhehao Shen, Penghao Wang, Zhuo Su, Yu Hong, Yingliang Zhang,
    Jingyi Yu, Lan Xu
  year: '2023'
  abstract: We have recently seen tremendous progress in photo-real human modeling
    and rendering. Yet, efficiently rendering realistic human performance and integrating
    it into the rasterization pipeline remains challenging. In this paper, we present
    HiFi4G, an explicit and compact Gaussian-based approach for high-fidelity human
    performance rendering from dense footage. Our core intuition is to marry the 3D
    Gaussian representation with non-rigid tracking, achieving a compact and compression-friendly
    representation. We first propose a dual-graph mechanism to obtain motion priors,
    with a coarse deformation graph for effective initialization and a fine-grained
    Gaussian graph to enforce subsequent constraints. Then, we utilize a 4D Gaussian
    optimization scheme with adaptive spatial-temporal regularizers to effectively
    balance the non-rigid prior and Gaussian updating. We also present a companion
    compression scheme with residual compensation for immersive experiences on various
    platforms. It achieves a substantial compression rate of approximately 25 times,
    with less than 2MB of storage per frame. Extensive experiments demonstrate the
    effectiveness of our approach, which significantly outperforms existing approaches
    in terms of optimization speed, rendering quality, and storage overhead.
  project_page: https://nowheretrix.github.io/HiFi4G/
  paper: https://arxiv.org/pdf/2312.03461.pdf
  code: null
  video: https://youtu.be/917WVr2EHh4
  tags:
  - Avatar
  - Project
  - Video
  thumbnail: assets/thumbnails/jiang2023hifi4g.jpg
  publication_date: '2023-12-06T12:36:53+00:00'
- id: yugay2023gaussianslam
  title: 'Gaussian-SLAM: Photo-realistic Dense SLAM with Gaussian Splatting'
  authors: Vladimir Yugay, Yue Li, Theo Gevers, Martin R. Oswald
  year: '2023'
  abstract: 'We present the first neural RGBD SLAM method capable of photorealistically
    reconstructing real-world scenes. Despite modern SLAM methods achieving impressive
    results on synthetic datasets, they still struggle with real-world datasets. Our
    approach utilizes 3D Gaussians as a primary unit for our scene representation
    to overcome the limitations of the previous methods. We observe that classical
    3D Gaussians are hard to use in a monocular setup: they can''t encode accurate
    geometry and are hard to optimize with single-view sequential supervision. By
    extending classical 3D Gaussians to encode geometry, and designing a novel scene
    representation and the means to grow, and optimize it, we propose a SLAM system
    capable of reconstructing and rendering real-world datasets without compromising
    on speed and efficiency. We show that Gaussian-SLAM can reconstruct and photorealistically
    render real-world scenes. We evaluate our method on common synthetic and real-world
    datasets and compare it against other state-of-the-art SLAM methods. Finally,
    we demonstrate, that the final 3D scene representation that we obtain can be rendered
    in Real-time thanks to the efficient Gaussian Splatting rendering.'
  project_page: https://vladimiryugay.github.io/gaussian_slam/
  paper: https://arxiv.org/pdf/2312.10070.pdf
  code: https://github.com/VladimirYugay/Gaussian-SLAM
  video: https://www.youtube.com/watch?v=RZK1o_ija7M
  tags:
  - Code
  - Project
  - SLAM
  - Video
  thumbnail: assets/thumbnails/yugay2023gaussianslam.jpg
  publication_date: '2023-12-06T10:47:53+00:00'
  date_source: arxiv
- id: zhou2023feature
  title: 'Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature
    Fields'
  authors: Shijie Zhou, Haoran Chang, Sicheng Jiang, Zhiwen Fan, Zehao Zhu, Dejia
    Xu, Pradyumna Chari, Suya You, Zhangyang Wang, Achuta Kadambi
  year: '2023'
  abstract: '3D scene representations have gained immense popularity in recent years.
    Methods that use Neural Radiance fields are versatile for traditional tasks such
    as novel view synthesis. In recent times, some work has emerged that aims to extend
    the functionality of NeRF beyond view synthesis, for semantically aware tasks
    such as editing and segmentation using 3D feature field distillation from 2D foundation
    models. However, these methods have two major limitations: (a) they are limited
    by the rendering speed of NeRF pipelines, and (b) implicitly represented feature
    fields suffer from continuity artifacts reducing feature quality. Recently, 3D
    Gaussian Splatting has shown state-of-the-art performance on real-time radiance
    field rendering. In this work, we go one step further: in addition to radiance
    field rendering, we enable 3D Gaussian splatting on arbitrary-dimension semantic
    features via 2D foundation model distillation. This translation is not straightforward:
    naively incorporating feature fields in the 3DGS framework encounters significant
    challenges, notably the disparities in spatial resolution and channel consistency
    between RGB images and feature maps. We propose architectural and training changes
    to efficiently avert this problem. Our proposed method is general, and our experiments
    showcase novel view semantic segmentation, language-guided editing and segment
    anything through learning feature fields from state-of-the-art 2D foundation models
    such as SAM and CLIP-LSeg. Across experiments, our distillation method is able
    to provide comparable or better results, while being significantly faster to both
    train and render. Additionally, to the best of our knowledge, we are the first
    method to enable point and bounding-box prompting for radiance field manipulation,
    by leveraging the SAM model.'
  project_page: https://feature-3dgs.github.io/
  paper: https://arxiv.org/pdf/2312.03203.pdf
  code: https://github.com/ShijieZhou-UCLA/feature-3dgs
  video: https://www.youtube.com/watch?v=h4zmQsCV_Qw
  tags:
  - Code
  - Editing
  - Inpainting
  - Language Embedding
  - Project
  - Segmentation
  - Video
  thumbnail: assets/thumbnails/zhou2023feature.jpg
  publication_date: '2023-12-06T00:46:30+00:00'
- id: hu2023gauhuman
  title: 'GauHuman: Articulated Gaussian Splatting from Monocular Human Videos'
  authors: Shoukang Hu Ziwei Liu
  year: '2023'
  abstract: We present, GauHuman, a 3D human model with Gaussian Splatting for both
    fast training (1~2 minutes) and real-time rendering (up to 189 FPS), compared
    with existing NeRF-based implicit representation modelling frameworks demanding
    hours of training and seconds of rendering per frame. Specifically, GauHuman encodes
    Gaussian Splatting in the canonical space and transforms 3D Gaussians from canonical
    space to posed space with linear blend skinning (LBS), in which effective pose
    and LBS refinement modules are designed to learn fine details of 3D humans under
    negligible computational cost. Moreover, to enable fast optimization of GauHuman,
    we initialize and prune 3D Gaussians with 3D human prior, while splitting/cloning
    via KL divergence guidance, along with a novel merge operation for further speeding
    up. Extensive experiments on ZJU_Mocap and MonoCap datasets demonstrate that GauHuman
    achieves state-of-the-art performance quantitatively and qualitatively with fast
    training and real-time rendering speed. Notably, without sacrificing rendering
    quality, GauHuman can fast model the 3D human performer with ~13k 3D Gaussians.
  project_page: https://skhu101.github.io/GauHuman/
  paper: https://arxiv.org/pdf/2312.02973.pdf
  code: https://github.com/skhu101/GauHuman
  video: https://www.youtube.com/embed/47772bgt5Xo
  tags:
  - Avatar
  - Code
  - Project
  - Video
  thumbnail: assets/thumbnails/hu2023gauhuman.jpg
  publication_date: '2023-12-05T18:59:14+00:00'
- id: dhamo2023headgas
  title: 'HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting'
  authors: Helisa Dhamo, Yinyu Nie, Arthur Moreau, Jifei Song, Richard Shaw, Yiren
    Zhou, Eduardo Pérez-Pellitero
  year: '2023'
  abstract: 3D head animation has seen major quality and runtime improvements over
    the last few years, particularly empowered by the advances in differentiable rendering
    and neural radiance fields. Real-time rendering is a highly desirable goal for
    real-world applications. We propose HeadGaS, the first model to use 3D Gaussian
    Splats (3DGS) for 3D head reconstruction and animation. In this paper we introduce
    a hybrid model that extends the explicit representation from 3DGS with a base
    of learnable latent features, which can be linearly blended with low-dimensional
    parameters from parametric head models to obtain expression-dependent final color
    and opacity values. We demonstrate that HeadGaS delivers state-of-the-art results
    in real-time inference frame rates, which surpasses baselines by up to ~2dB, while
    accelerating rendering speed by over x10.
  project_page: null
  paper: https://arxiv.org/pdf/2312.02902.pdf
  code: null
  video: https://www.youtube.com/watch?v=fetxolufsgQ
  tags:
  - Avatar
  - Video
  thumbnail: assets/thumbnails/dhamo2023headgas.jpg
  publication_date: '2023-12-05T17:19:22+00:00'
- id: xu2023gaussian
  title: 'Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians'
  authors: Yuelang Xu, Benwang Chen, Zhe Li, Hongwen Zhang, Lizhen Wang, Zerong Zheng,
    Yebin Liu
  year: '2023'
  abstract: Creating high-fidelity 3D head avatars has always been a research hotspot,
    but there remains a great challenge under lightweight sparse view setups. In this
    paper, we propose Gaussian Head Avatar represented by controllable 3D Gaussians
    for high-fidelity head avatar modeling. We optimize the neutral 3D Gaussians and
    a fully learned MLP-based deformation field to capture complex expressions. The
    two parts benefit each other, thereby our method can model fine-grained dynamic
    details while ensuring expression accuracy. Furthermore, we devise a well-designed
    geometry-guided initialization strategy based on implicit SDF and Deep Marching
    Tetrahedra for the stability and convergence of the training procedure. Experiments
    show our approach outperforms other state-of-the-art sparse-view methods, achieving
    ultra high-fidelity rendering quality at 2K resolution even under exaggerated
    expressions.
  project_page: https://yuelangx.github.io/gaussianheadavatar/
  paper: https://arxiv.org/pdf/2312.03029.pdf
  code: https://github.com/YuelangX/Gaussian-Head-Avatar
  video: https://www.youtube.com/watch?v=kvrrI3EoM5g
  tags:
  - Avatar
  - Code
  - Project
  - Video
  thumbnail: assets/thumbnails/xu2023gaussian.jpg
  publication_date: '2023-12-05T11:01:44+00:00'
- id: zheng2023gpsgaussian
  title: 'GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time
    Human Novel View Synthesis'
  authors: Shunyuan Zheng, Boyao Zhou, Ruizhi Shao, Boning Liu, Shengping Zhang, Liqiang
    Nie, Yebin Liu
  year: '2023'
  abstract: We present a new approach, termed GPS-Gaussian, for synthesizing novel
    views of a character in a real-time manner. The proposed method enables 2K-resolution
    rendering under a sparse-view camera setting. Unlike the original Gaussian Splatting
    or neural implicit rendering methods that necessitate per-subject optimizations,
    we introduce Gaussian parameter maps defined on the source views and regress directly
    Gaussian Splatting properties for instant novel view synthesis without any fine-tuning
    or optimization. To this end, we train our Gaussian parameter regression module
    on a large amount of human scan data, jointly with a depth estimation module to
    lift 2D parameter maps to 3D space. The proposed framework is fully differentiable
    and experiments on several datasets demonstrate that our method outperforms state-of-the-art
    methods while achieving an exceeding rendering speed.
  project_page: https://shunyuanzheng.github.io/GPS-Gaussian
  paper: https://arxiv.org/pdf/2312.02155.pdf
  code: https://github.com/ShunyuanZheng/GPS-Gaussian
  video: https://youtu.be/TBIekcqt0j0
  tags:
  - Avatar
  - Code
  - Project
  - Video
  thumbnail: assets/thumbnails/zheng2023gpsgaussian.jpg
  publication_date: '2023-12-04T18:59:55+00:00'
- id: pokhariya2023manus
  title: 'MANUS: Markerless Hand-Object Grasp Capture using Articulated 3D Gaussians'
  authors: Chandradeep Pokhariya, Ishaan N Shah, Angela Xing, Zekun Li, Kefan Chen,
    Avinash Sharma, Srinath Sridhar
  year: '2023'
  abstract: Understanding how we grasp objects with our hands has important applications
    in areas like robotics and mixed reality. However, this challenging problem requires
    accurate modeling of the contact between hands and objects. To capture grasps,
    existing methods use skeletons, meshes, or parametric models that can cause misalignments
    resulting in inaccurate contacts. We present MANUS, a method for Markerless Hand-Object
    Grasp Capture using Articulated 3D Gaussians. We build a novel articulated 3D
    Gaussians representation that extends 3D Gaussian splatting for high-fidelity
    representation of articulating hands. Since our representation uses Gaussian primitives,
    it enables us to efficiently and accurately estimate contacts between the hand
    and the object. For the most accurate results, our method requires tens of camera
    views that current datasets do not provide. We therefore build MANUS-Grasps, a
    new dataset that contains hand-object grasps viewed from 53 cameras across 30+
    scenes, 3 subjects, and comprising over 7M frames. In addition to extensive qualitative
    results, we also show that our method outperforms others on a quantitative contact
    evaluation method that uses paint transfer from the object to the hand.
  project_page: https://ivl.cs.brown.edu/research/manus.html
  paper: https://arxiv.org/pdf/2312.02137.pdf
  code: https://github.com/brown-ivl/manus
  video: null
  tags:
  - Code
  - Misc
  - Project
  thumbnail: assets/thumbnails/pokhariya2023manus.jpg
  publication_date: '2023-12-04T18:56:22+00:00'
- id: hu2023gaussianavatar
  title: 'GaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video
    via Animatable 3D Gaussians'
  authors: Liangxiao Hu, Hongwen Zhang, Yuxiang Zhang, Boyao Zhou, Boning Liu, Shengping
    Zhang, Liqiang Nie
  year: '2023'
  abstract: We present GaussianAvatar, an efficient approach to creating realistic
    human avatars with dynamic 3D appearances from a single video. We start by introducing
    animatable 3D Gaussians to explicitly represent humans in various poses and clothing
    styles. Such an explicit and animatable representation can fuse 3D appearances
    more efficiently and consistently from 2D observations. Our representation is
    further augmented with dynamic properties to support pose-dependent appearance
    modeling, where a dynamic appearance network along with an optimizable feature
    tensor is designed to learn the motion-to-appearance mapping. Moreover, by leveraging
    the differentiable motion condition, our method enables a joint optimization of
    motions and appearances during avatar modeling, which helps to tackle the long-standing
    issue of inaccurate motion estimation in monocular settings. The efficacy of GaussianAvatar
    is validated on both the public dataset and our collected dataset, demonstrating
    its superior performances in terms of appearance quality and rendering efficiency.
  project_page: https://huliangxiao.github.io/GaussianAvatar
  paper: https://arxiv.org/pdf/2312.02134.pdf
  code: https://github.com/huliangxiao/GaussianAvatar
  video: https://www.youtube.com/watch?v=a4g8Z9nCF-k&t=1s
  tags:
  - Avatar
  - Code
  - Project
  - Video
  thumbnail: assets/thumbnails/hu2023gaussianavatar.jpg
  publication_date: '2023-12-04T18:55:45+00:00'
- id: keetha2023splatam
  title: 'SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM'
  authors: Nikhil Keetha, Jay Karhade, Krishna Murthy Jatavallabhula, Gengshan Yang,
  year: '2023'
  abstract: Dense simultaneous localization and mapping (SLAM) is pivotal for embodied
    scene understanding. Recent work has shown that 3D Gaussians enable high-quality
    reconstruction and real-time rendering of scenes using multiple posed cameras.
    In this light, we show for the first time that representing a scene by 3D Gaussians
    can enable dense SLAM using a single unposed monocular RGB-D camera. Our method,
    SplaTAM, addresses the limitations of prior radiance field-based representations,
    including fast rendering and optimization, the ability to determine if areas have
    been previously mapped, and structured map expansion by adding more Gaussians.
    We employ an online tracking and mapping pipeline while tailoring it to specifically
    use an underlying Gaussian representation and silhouette-guided optimization via
    differentiable rendering. Extensive experiments show that SplaTAM achieves up
    to 2× state-of-theart performance in camera pose estimation, map construction,
    and novel-view synthesis, demonstrating its superiority over existing approaches,
    while allowing real-time rendering of a high-resolution dense 3D map.
  project_page: https://spla-tam.github.io/
  paper: https://arxiv.org/pdf/2312.02126.pdf
  code: https://github.com/spla-tam/SplaTAM
  video: https://www.youtube.com/watch?v=35SX8DTdQLs
  tags:
  - Code
  - Project
  - SLAM
  - Video
  thumbnail: assets/thumbnails/keetha2023splatam.jpg
  publication_date: '2023-12-04T18:53:24+00:00'
- id: ye2023mathematical
  title: Mathematical Supplement for the gsplat Library
  authors: Vickie Ye, Angjoo Kanazawa
  year: '2023'
  abstract: This report provides the mathematical details of the gsplat library, a
    modular toolbox for efficient differentiable Gaussian splatting, as proposed by
    Kerbl et al. It provides a self-contained reference for the computations involved
    in the forward and backward passes of differentiable Gaussian splatting. To facilitate
    practical usage and development, we provide a user friendly Python API that exposes
    each component of the forward and backward passes in rasterization of [gsplat](https://github.com/nerfstudio-project/gsplat).
  project_page: null
  paper: https://arxiv.org/pdf/2312.02121.pdf
  code: null
  video: null
  tags:
  - Misc
  thumbnail: assets/thumbnails/ye2023mathematical.jpg
  publication_date: '2023-12-04T18:50:41+00:00'
- id: qian2023gaussianavatars
  title: 'GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians'
  authors: Shenhan Qian, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Simon
    Giebenhain, Matthias Nießner
  year: '2023'
  abstract: We introduce GaussianAvatars, a new method to create photorealistic head
    avatars that are fully controllable in terms of expression, pose, and viewpoint.
    The core idea is a dynamic 3D representation based on 3D Gaussian splats that
    are rigged to a parametric morphable face model. This combination facilitates
    photorealistic rendering while allowing for precise animation control via the
    underlying parametric model, e.g., through expression transfer from a driving
    sequence or by manually changing the morphable model parameters. We parameterize
    each splat by a local coordinate frame of a triangle and optimize for explicit
    displacement offset to obtain a more accurate geometric representation. During
    avatar reconstruction, we jointly optimize for the morphable model parameters
    and Gaussian splat parameters in an end-to-end fashion. We demonstrate the animation
    capabilities of our photorealistic avatar in several challenging scenarios. For
    instance, we show reenactments from a driving video, where our method outperforms
    existing works by a significant margin.
  project_page: https://shenhanqian.github.io/gaussian-avatars
  paper: https://arxiv.org/pdf/2312.02069
  code: https://github.com/ShenhanQian/GaussianAvatars
  video: https://youtu.be/lVEY78RwU_I
  tags:
  - Avatar
  - Code
  - Project
  - Video
  thumbnail: assets/thumbnails/qian2023gaussianavatars.jpg
  publication_date: '2023-12-04T17:28:35+00:00'
- id: huang2023scgs
  title: 'SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes'
  authors: Yi-Hua Huang, Yang-Tian Sun, Ziyi Yang, Xiaoyang Lyu, Yan-Pei Cao, Xiaojuan
    Qi
  year: '2023'
  abstract: 'Novel view synthesis for dynamic scenes is still a challenging problem
    in computer vision and graphics. Recently, Gaussian splatting has emerged as a
    robust technique to represent static scenes and enable high-quality and real-time
    novel view synthesis. Building upon this technique, we propose a new representation
    that explicitly decomposes the motion and appearance of dynamic scenes into sparse
    control points and dense Gaussians, respectively. Our key idea is to use sparse
    control points, significantly fewer in number than the Gaussians, to learn compact
    6 DoF transformation bases, which can be locally interpolated through learned
    interpolation weights to yield the motion field of 3D Gaussians. We employ a deformation
    MLP to predict time-varying 6 DoF transformations for each control point, which
    reduces learning complexities, enhances learning abilities, and facilitates obtaining
    temporal and spatial coherent motion patterns. Then, we jointly learn the 3D Gaussians,
    the canonical space locations of control points, and the deformation MLP to reconstruct
    the appearance, geometry, and dynamics of 3D scenes. During learning, the location
    and number of control points are adaptively adjusted to accommodate varying motion
    complexities in different regions, and an ARAP loss following the principle of
    as rigid as possible is developed to enforce spatial continuity and local rigidity
    of learned motions. Finally, thanks to the explicit sparse motion representation
    and its decomposition from appearance, our method can enable user-controlled motion
    editing while retaining high-fidelity appearances. Extensive experiments demonstrate
    that our approach outperforms existing approaches on novel view synthesis with
    a high rendering speed and enables novel appearance-preserved motion editing applications.
    Project page: https://yihua7.github.io/SC-GS-web/

    '
  project_page: https://yihua7.github.io/SC-GS-web/
  paper: https://arxiv.org/pdf/2312.14937.pdf
  code: https://github.com/yihua7/SC-GS
  video: https://youtu.be/CYQYX_0xi5E
  tags:
  - Code
  - Dynamic
  - Project
  - Video
  thumbnail: assets/thumbnails/huang2023scgs.jpg
  publication_date: '2023-12-04T11:57:14+00:00'
  date_source: arxiv
- id: wang2023gaussianhead
  title: 'GaussianHead: High-fidelity Head Avatars with Learnable Gaussian Derivation'
  authors: Jie Wang, Jiu-Cheng Xie, Xianyan Li, Chi-Man Pun, Feng Xu, Hao Gao
  year: '2023'
  abstract: Constructing vivid 3D head avatars for given subjects and realizing a
    series of animations on them is valuable yet challenging. This paper presents
    GaussianHead, which models the actional human head with anisotropic 3D Gaussians.
    In our framework, a motion deformation field and multi-resolution tri-plane are
    constructed respectively to deal with the head's dynamic geometry and complex
    texture. Notably, we impose an exclusive derivation scheme on each Gaussian, which
    generates its multiple doppelgangers through a set of learnable parameters for
    position transformation. With this design, we can compactly and accurately encode
    the appearance information of Gaussians, even those fitting the head's particular
    components with sophisticated structures. In addition, an inherited derivation
    strategy for newly added Gaussians is adopted to facilitate training acceleration.
    Extensive experiments show that our method can produce high-fidelity renderings,
    outperforming state-of-the-art approaches in reconstruction, cross-identity reenactment,
    and novel view synthesis tasks.
  project_page: https://chiehwangs.github.io/gaussian-head-page/
  paper: https://arxiv.org/pdf/2312.01632.pdf
  code: https://github.com/chiehwangs/gaussian-head
  video: null
  tags:
  - Avatar
  - Code
  - Project
  thumbnail: assets/thumbnails/wang2023gaussianhead.jpg
  publication_date: '2023-12-04T05:24:45+00:00'
- id: xiang2023flashavatar
  title: 'FlashAvatar: High-fidelity Head Avatar with Efficient Gaussian Embedding'
  authors: Jun Xiang, Xuan Gao, Yudong Guo, Juyong Zhang
  year: '2023'
  abstract: We propose FlashAvatar, a novel and lightweight 3D animatable avatar representation
    that could reconstruct a digital avatar from a short monocular video sequence
    in minutes and render high-fidelity photo-realistic images at 300FPS on a consumer-grade
    GPU. To achieve this, we maintain a uniform 3D Gaussian field embedded in the
    surface of a parametric face model and learn extra spatial offset to model non-surface
    regions and subtle facial details. While full use of geometric priors can capture
    high-frequency facial details and preserve exaggerated expressions, proper initialization
    can help reduce the number of Gaussians, thus enabling super-fast rendering speed.
    Extensive experimental results demonstrate that FlashAvatar outperforms existing
    works regarding visual quality and personalized details and is almost an order
    of magnitude faster in rendering speed.
  project_page: https://ustc3dv.github.io/FlashAvatar/
  paper: https://arxiv.org/pdf/2312.02214v2
  code: https://github.com/USTC3DV/FlashAvatar-code
  video: null
  tags:
  - Avatar
  - Code
  - Project
  thumbnail: assets/thumbnails/xiang2023flashavatar.jpg
  publication_date: '2023-12-03T07:23:53+00:00'
- id: das2023neural
  title: Neural Parametric Gaussians for Monocular Non-Rigid Object Reconstruction
  authors: Devikalyan Das, Christopher Wewer, Raza Yunus, Eddy Ilg, Jan Eric Lenssen
  year: '2023'
  abstract: 'Reconstructing dynamic objects from monocular videos is a severely underconstrained
    and challenging problem, and recent work has approached it in various directions.
    However, owing to the ill-posed nature of this problem, there has been no solution
    that can provide consistent, highquality novel views from camera positions that
    are significantly different from the training views. In this work, we introduce
    Neural Parametric Gaussians (NPGs) to take on this challenge by imposing a two-stage
    approach: first, we fit a low-rank neural deformation model, which then is used
    as regularization for non-rigid reconstruction in the second stage. The first
    stage learns the object’s deformations such that it preserves consistency in novel
    views. The second stage obtains high reconstruction quality by optimizing 3D Gaussians
    that are driven by the coarse model. To this end, we introduce a local 3D Gaussian
    representation, where temporally shared Gaussians are anchored in and deformed
    by local oriented volumes. The resulting combined model can be rendered as radiance
    fields, resulting in high-quality photo-realistic reconstructions of the non-rigidly
    deforming objects, maintaining 3D consistency across novel views. We demonstrate
    that NPGs achieve superior results compared to previous works, especially in challenging
    scenarios with few multi-view cues.'
  project_page: https://geometric-rl.mpi-inf.mpg.de/npg/
  paper: https://arxiv.org/pdf/2312.01196.pdf
  code: https://github.com/DevikalyanDas/npgs
  video: https://youtu.be/ModbrI0GORU
  tags:
  - Code
  - Dynamic
  - Monocular
  - Project
  - Video
  thumbnail: assets/thumbnails/das2023neural.jpg
  publication_date: '2023-12-02T18:06:24+00:00'
- id: durvasula2024distwar
  title: 'DISTWAR: Fast Differentiable Rendering on Raster-based Rendering Pipelines'
  authors: Sankeerth Durvasula, Adrian Zhao, Fan Chen, Ruofan Liang, Pawan Kumar Sanjaya,
    Nandita Vijaykumar
  year: '2024'
  abstract: 'Differentiable rendering is a technique used in an important emerging
    class of visual computing applications that involves representing a 3D scene as
    a model that is trained from 2D images using gradient descent. Recent works (e.g.
    3D Gaussian Splatting) use a rasterization pipeline to enable rendering high quality
    photo-realistic imagery at high speeds from these learned 3D models. These methods
    have been demonstrated to be very promising, providing state-of-art quality for
    many important tasks. However, training a model to represent a scene is still
    a time-consuming task even when using powerful GPUs. In this work, we observe
    that the gradient computation phase during training is a significant bottleneck
    on GPUs due to the large number of atomic operations that need to be processed.
    These atomic operations overwhelm atomic units in the L2 partitions causing stalls.
    To address this challenge, we leverage the observations that during the gradient
    computation: (1) for most warps, all threads atomically update the same memory
    locations; and (2) warps generate varying amounts of atomic traffic (since some
    threads may be inactive). We propose DISTWAR, a software-approach to accelerate
    atomic operations based on two key ideas: First, we enable warp-level reduction
    of threads at the SM sub-cores using registers to leverage the locality in intra-warp
    atomic updates. Second, we distribute the atomic computation between the warp-level
    reduction at the SM and the L2 atomic units to increase the throughput of atomic
    computation. Warps with many threads performing atomic updates to the same memory
    locations are scheduled at the SM, and the rest using L2 atomic units. We implement
    DISTWAR using existing warp-level primitives. We evaluate DISTWAR on widely used
    raster-based differentiable rendering workloads. We demonstrate significant speedups
    of 2.44x on average (up to 5.7x).'
  project_page: null
  paper: https://arxiv.org/pdf/2401.05345.pdf
  code: https://github.com/Accelsnow/gaussian-splatting-distwar
  video: null
  tags:
  - Acceleration
  - Code
  - Rendering
  thumbnail: assets/thumbnails/durvasula2024distwar.jpg
  publication_date: '2023-12-01T19:56:08+00:00'
- id: cen2023segment
  title: Segment Any 3D Gaussians
  authors: Jiazhong Cen, Jiemin Fang, Chen Yang, Lingxi Xie, Xiaopeng Zhang, Wei Shen,
    Qi Tian
  year: '2023'
  abstract: Interactive 3D segmentation in radiance fields is an appealing task since
    its importance in 3D scene understanding and manipulation. However, existing methods
    face challenges in either achieving fine-grained, multi-granularity segmentation
    or contending with substantial computational overhead, inhibiting real-time interaction.
    In this paper, we introduce Segment Any 3D GAussians (SAGA), a novel 3D interactive
    segmentation approach that seamlessly blends a 2D segmentation foundation model
    with 3D Gaussian Splatting (3DGS), a recent breakthrough of radiance fields. SAGA
    efficiently embeds multi-granularity 2D segmentation results generated by the
    segmentation foundation model into 3D Gaussian point features through well-designed
    contrastive training. Evaluation on existing benchmarks demonstrates that SAGA
    can achieve competitive performance with state-of-the-art methods. Moreover, SAGA
    achieves multi-granularity segmentation and accommodates various prompts, including
    points, scribbles, and 2D masks. Notably, SAGA can finish the 3D segmentation
    within milliseconds, achieving nearly 1000× acceleration1 compared to previous
    SOTA.
  project_page: https://jumpat.github.io/SAGA/
  paper: https://arxiv.org/pdf/2312.00860.pdf
  code: https://github.com/Jumpat/SegAnyGAussians
  video: null
  tags:
  - Code
  - Editing
  - Project
  - Segmentation
  thumbnail: assets/thumbnails/cen2023segment.jpg
  publication_date: '2023-12-01T17:15:24+00:00'
  date_source: arxiv
- id: ye2023gaussian
  title: 'Gaussian Grouping: Segment and Edit Anything in 3D Scenes'
  authors: Mingqiao Ye, Martin Danelljan, Fisher Yu, Lei Ke
  year: '2023'
  abstract: The recent Gaussian Splatting achieves high-quality and real-time novel-view
    synthesis of the 3D scenes. However, it is solely concentrated on the appearance
    and geometry modeling, while lacking in fine-grained object-level scene understanding.
    To address this issue, we propose Gaussian Grouping, which extends Gaussian Splatting
    to jointly reconstruct and segment anything in open-world 3D scenes. We augment
    each Gaussian with a compact Identity Encoding, allowing the Gaussians to be grouped
    according to their object instance or stuff membership in the 3D scene. Instead
    of resorting to expensive 3D labels, we supervise the Identity Encodings during
    the differentiable rendering by leveraging the 2D mask predictions by SAM, along
    with introduced 3D spatial consistency regularization. Comparing to the implicit
    NeRF representation, we show that the discrete and grouped 3D Gaussians can reconstruct,
    segment and edit anything in 3D with high visual quality, fine granularity and
    efficiency. Based on Gaussian Grouping, we further propose a local Gaussian Editing
    scheme, which shows efficacy in versatile scene editing applications, including
    3D object removal, inpainting, colorization and scene recomposition.
  project_page: https://ymq2017.github.io/gaussian-grouping/
  paper: https://arxiv.org/pdf/2312.00732.pdf
  code: https://github.com/lkeab/gaussian-grouping
  video: null
  tags:
  - Code
  - Editing
  - Project
  - Segmentation
  thumbnail: assets/thumbnails/ye2023gaussian.jpg
  publication_date: '2023-12-01T17:09:31+00:00'
- id: zhu2023fsgs
  title: 'FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting'
  authors: Zehao Zhu, Zhiwen Fan, Yifan Jiang, Zhangyang Wang
  year: '2023'
  abstract: Novel view synthesis from limited observations remains an important and
    persistent task. However, high efficiency in existing NeRF-based few-shot view
    synthesis is often compromised to obtain an accurate 3D representation. To address
    this challenge, we propose a few-shot view synthesis framework based on 3D Gaussian
    Splatting that enables real-time and photo-realistic view synthesis with as few
    as three training views. The proposed method, dubbed FSGS, handles the extremely
    sparse initialized SfM points with a thoughtfully designed Gaussian Unpooling
    process. Our method iteratively distributes new Gaussians around the most representative
    locations, subsequently infilling local details in vacant areas. We also integrate
    a large-scale pre-trained monocular depth estimator within the Gaussians optimization
    process, leveraging online augmented views to guide the geometric optimization
    towards an optimal solution. Starting from sparse points observed from limited
    input viewpoints, our FSGS can accurately grow into unseen regions, comprehensively
    covering the scene and boosting the rendering quality of novel views. Overall,
    FSGS achieves state-of-the-art performance in both accuracy and rendering efficiency
    across diverse datasets, including LLFF, Mip-NeRF360, and Blender
  project_page: https://zehaozhu.github.io/FSGS/
  paper: https://arxiv.org/pdf/2312.00451.pdf
  code: https://github.com/VITA-Group/FSGS
  video: https://www.youtube.com/watch?v=CarJgsx3DQY
  tags:
  - Code
  - Project
  - Sparse
  - Video
  thumbnail: assets/thumbnails/zhu2023fsgs.jpg
  publication_date: '2023-12-01T09:30:02+00:00'
- id: chen2023neusg
  title: 'NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting
    Guidance'
  authors: Hanlin Chen, Chen Li, Gim Hee Lee
  year: '2023'
  abstract: Existing neural implicit surface reconstruction methods have achieved
    impressive performance in multi-view 3D reconstruction by leveraging explicit
    geometry priors such as depth maps or point clouds as regularization. However,
    the reconstruction results still lack fine details because of the over-smoothed
    depth map or sparse point cloud. In this work, we propose a neural implicit surface
    reconstruction pipeline with guidance from 3D Gaussian Splatting to recover highly
    detailed surfaces. The advantage of 3D Gaussian Splatting is that it can generate
    dense point clouds with detailed structure. Nonetheless, a naive adoption of 3D
    Gaussian Splatting can fail since the generated points are the centers of 3D Gaussians
    that do not necessarily lie on the surface. We thus introduce a scale regularizer
    to pull the centers close to the surface by enforcing the 3D Gaussians to be extremely
    thin. Moreover, we propose to refine the point cloud from 3D Gaussians Splatting
    with the normal priors from the surface predicted by neural implicit models instead
    of using a fixed set of points as guidance. Consequently, the quality of surface
    reconstruction improves from the guidance of the more accurate 3D Gaussian splatting.
    By jointly optimizing the 3D Gaussian Splatting and the neural implicit model,
    our approach benefits from both representations and generates complete surfaces
    with intricate details. Experiments on Tanks and Temples verify the effectiveness
    of our proposed method.
  project_page: null
  paper: https://arxiv.org/pdf/2312.00846.pdf
  code: null
  video: null
  tags:
  - Meshing
  thumbnail: assets/thumbnails/chen2023neusg.jpg
  publication_date: '2023-12-01T07:04:47+00:00'
- id: xiong2023sparsegs
  title: 'SparseGS: Real-Time 360° Sparse View Synthesis using Gaussian Splatting'
  authors: Haolin Xiong, Sairisheek Muttukuru, Rishi Upadhyay, Pradyumna Chari, Achuta
    Kadambi
  year: '2023'
  abstract: The problem of novel view synthesis has grown significantly in popularity
    recently with the introduction of Neural Radiance Fields (NeRFs) and other implicit
    scene representation methods. A recent advance, 3D Gaussian Splatting (3DGS),
    leverages an explicit representation to achieve real-time rendering with high-quality
    results. However, 3DGS still requires an abundance of training views to generate
    a coherent scene representation. In few shot settings, similar to NeRF, 3DGS tends
    to overfit to training views, causing background collapse and excessive floaters,
    especially as the number of training views are reduced. We propose a method to
    enable training coherent 3DGS-based radiance fields of 360 scenes from sparse
    training views. We find that using naive depth priors is not sufficient and integrate
    depth priors with generative and explicit constraints to reduce background collapse,
    remove floaters, and enhance consistency from unseen viewpoints. Experiments show
    that our method outperforms base 3DGS by up to 30.5% and NeRF-based methods by
    up to 15.6% in LPIPS on the MipNeRF-360 dataset with substantially less training
    and inference cost.
  project_page: https://formycat.github.io/SparseGS-Real-Time-360-Sparse-View-Synthesis-using-Gaussian-Splatting/
  paper: https://arxiv.org/pdf/2312.00206.pdf
  code: https://github.com/ForMyCat/SparseGS
  video: null
  tags:
  - 360 degree
  - Code
  - Project
  - Sparse
  thumbnail: assets/thumbnails/xiong2023sparsegs.jpg
  publication_date: '2023-11-30T21:38:22+00:00'
- id: kratimenos2023dynmf
  title: 'DynMF: Neural Motion Factorization for Real-time Dynamic View Synthesis
    with 3D Gaussian Splatting'
  authors: Agelos Kratimenos, Jiahui Lei, Kostas Daniilidis
  year: '2023'
  abstract: Accurately and efficiently modeling dynamic scenes and motions is considered
    so challenging a task due to temporal dynamics and motion complexity. To address
    these challenges, we propose DynMF, a compact and efficient representation that
    decomposes a dynamic scene into a few neural trajectories. We argue that the per-point
    motions of a dynamic scene can be decomposed into a small set of explicit or learned
    trajectories. Our carefully designed neural framework consisting of a tiny set
    of learned basis queried only in time allows for rendering speed similar to 3D
    Gaussian Splatting, surpassing 120 FPS, while at the same time, requiring only
    double the storage compared to static scenes. Our neural representation adequately
    constrains the inherently underconstrained motion field of a dynamic scene leading
    to effective and fast optimization. This is done by biding each point to motion
    coefficients that enforce the per-point sharing of basis trajectories. By carefully
    applying a sparsity loss to the motion coefficients, we are able to disentangle
    the motions that comprise the scene, independently control them, and generate
    novel motion combinations that have never been seen before. We can reach state-of-the-art
    render quality within just 5 minutes of training and in less than half an hour,
    we can synthesize novel views of dynamic scenes with superior photorealistic quality.
    Our representation is interpretable, efficient, and expressive enough to offer
    real-time view synthesis of complex dynamic scene motions, in monocular and multi-view
    scenarios.
  project_page: https://agelosk.github.io/dynmf/
  paper: https://arxiv.org/pdf/2312.00112.pdf
  code: null
  video: https://agelosk.github.io/dynmf/img/presentation.mp4#t=0.8
  tags:
  - Dynamic
  - Project
  - Video
  thumbnail: assets/thumbnails/kratimenos2023dynmf.jpg
  publication_date: '2023-11-30T18:59:11+00:00'
- id: p2023mdsplatting
  title: 'MD-Splatting: Learning Metric Deformation from 4D Gaussians in Highly Deformable
    Scenes'
  authors: Bardienus P. Duisterhof, Zhao Mandi, Yunchao Yao, Jia-Wei Liu, Mike Zheng
    Shou, Shuran Song, Jeffrey Ichnowski
  year: '2023'
  abstract: Accurate 3D tracking in highly deformable scenes with occlusions and shadows
    can facilitate new applications in robotics, augmented reality, and generative
    AI. However, tracking under these conditions is extremely challenging due to the
    ambiguity that arises with large deformations, shadows, and occlusions. We introduce
    MD-Splatting, an approach for simultaneous 3D tracking and novel view synthesis,
    using video captures of a dynamic scene from various camera poses. MD-Splatting
    builds on recent advances in Gaussian splatting, a method that learns the properties
    of a large number of Gaussians for state-of-the-art and fast novel view synthesis.
    MD-Splatting learns a deformation function to project a set of Gaussians with
    non-metric, thus canonical, properties into metric space. The deformation function
    uses a neural-voxel encoding and a multilayer perceptron (MLP) to infer Gaussian
    position, rotation, and a shadow scalar. We enforce physics-inspired regularization
    terms based on local rigidity, conservation of momentum, and isometry, which leads
    to trajectories with smaller trajectory errors. MD-Splatting achieves high-quality
    3D tracking on highly deformable scenes with shadows and occlusions. Compared
    to state-of-the-art, we improve 3D tracking by an average of 23.9 %, while simultaneously
    achieving high-quality novel view synthesis. With sufficient texture such as in
    scene 6, MD-Splatting achieves a median tracking error of 3.39 mm on a cloth of
    1 x 1 meters in size
  project_page: https://md-splatting.github.io/
  paper: https://arxiv.org/pdf/2312.00583
  code: https://github.com/momentum-robotics-lab/md-splatting
  video: null
  tags:
  - Code
  - Dynamic
  - Project
  thumbnail: assets/thumbnails/p2023mdsplatting.jpg
  publication_date: '2023-11-30T18:53:03+00:00'
- id: lu2023scaffoldgs
  title: 'Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering'
  authors: Tao Lu, Mulin Yu, Linning Xu, Yuanbo Xiangli, Limin Wang, Dahua Lin, Bo
    Dai
  year: '2023'
  abstract: Neural rendering methods have significantly advanced photo-realistic 3D
    scene rendering in various academic and industrial applications. The recent 3D
    Gaussian Splatting method has achieved the state-of-the-art rendering quality
    and speed combining the benefits of both primitive-based representations and volumetric
    representations. However, it often leads to heavily redundant Gaussians that try
    to fit every training view, neglecting the underlying scene geometry. Consequently,
    the resulting model becomes less robust to significant view changes, texture-less
    area and lighting effects. We introduce Scaffold-GS, which uses anchor points
    to distribute local 3D Gaussians, and predicts their attributes on-the-fly based
    on viewing direction and distance within the view frustum. Anchor growing and
    pruning strategies are developed based on the importance of neural Gaussians to
    reliably improve the scene coverage. We show that our method effectively reduces
    redundant Gaussians while delivering high-quality rendering. We also demonstrates
    an enhanced capability to accommodate scenes with varying levels-of-detail and
    view-dependent observations, without sacrificing the rendering speed.
  project_page: https://city-super.github.io/scaffold-gs/
  paper: https://arxiv.org/pdf/2312.00109.pdf
  code: https://github.com/city-super/Scaffold-GS
  video: null
  tags:
  - Code
  - Densification
  - Project
  - Rendering
  thumbnail: assets/thumbnails/lu2023scaffoldgs.jpg
  publication_date: '2023-11-30T17:58:57+00:00'
- id: chen2023periodic
  title: 'Periodic Vibration Gaussian: Dynamic Urban Scene Reconstruction and Real-time
    Rendering'
  authors: Yurui Chen, Chun Gu, Junzhe Jiang, Xiatian Zhu, Li Zhang
  year: '2023'
  abstract: Modeling dynamic, large-scale urban scenes is challenging due to their
    highly intricate geometric structures and unconstrained dynamics in both space
    and time. Prior methods often employ high-level architectural priors, separating
    static and dynamic elements, resulting in suboptimal capture of their synergistic
    interactions. To address this challenge, we present a unified representation model,
    called Periodic Vibration Gaussian (PVG). PVG builds upon the efficient 3D Gaussian
    splatting technique, originally designed for static scene representation, by introducing
    periodic vibration-based temporal dynamics. This innovation enables PVG to elegantly
    and uniformly represent the characteristics of various objects and elements in
    dynamic urban scenes. To enhance temporally coherent representation learning with
    sparse training data, we introduce a novel flow-based temporal smoothing mechanism
    and a position-aware adaptive control strategy. Extensive experiments on Waymo
    Open Dataset and KITTI benchmarks demonstrate that PVG surpasses state-of-the-art
    alternatives in both reconstruction and novel view synthesis for both dynamic
    and static scenes. Notably, PVG achieves this without relying on manually labeled
    object bounding boxes or expensive optical flow estimation. Moreover, PVG exhibits
    50/6000-fold acceleration in training/rendering over the best alternative.
  project_page: https://fudan-zvg.github.io/PVG/
  paper: https://arxiv.org/pdf/2311.18561.pdf
  code: https://github.com/fudan-zvg/PVG
  video: null
  tags:
  - Autonomous Driving
  - Code
  - Misc
  - Project
  thumbnail: assets/thumbnails/chen2023periodic.jpg
  publication_date: '2023-11-30T13:53:50+00:00'
- id: shi2024language
  title: Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding
  authors: Jin-Chuan Shi, Miao Wang, Hao-Bin Duan, Shao-Hua Guan
  year: '2024'
  abstract: Open-vocabulary querying in 3D space is challenging but essential for
    scene understanding tasks such as object localization and segmentation. Language-embedded
    scene representations have made progress by incorporating language features into
    3D spaces. However, their efficacy heavily depends on neural networks that are
    resource-intensive in training and rendering. Although recent 3D Gaussians offer
    efficient and high-quality novel view synthesis, directly embedding language features
    in them leads to prohibitive memory usage and decreased performance. In this work,
    we introduce Language Embedded 3D Gaussians, a novel scene representation for
    open-vocabulary query tasks. Instead of embedding high-dimensional raw semantic
    features on 3D Gaussians, we propose a dedicated quantization scheme that drastically
    alleviates the memory requirement, and a novel embedding procedure that achieves
    smoother yet high accuracy query, countering the multi-view feature inconsistencies
    and the high-frequency inductive bias in point-based representations. Our comprehensive
    experiments show that our representation achieves the best visual quality and
    language querying accuracy across current language-embedded representations, while
    maintaining real-time rendering frame rates on a single desktop GPU.
  project_page: https://buaavrcg.github.io/LEGaussians/
  paper: https://arxiv.org/pdf/2311.18482.pdf
  code: https://github.com/buaavrcg/LEGaussians
  video: null
  tags:
  - Code
  - Language Embedding
  - Project
  - Segmentation
  thumbnail: assets/thumbnails/shi2024language.jpg
  publication_date: '2023-11-30T11:50:07+00:00'
- id: navaneet2023compact3d
  title: 'Compact3D: Compressing Gaussian Splat Radiance Field Models with Vector
    Quantization'
  authors: KL Navaneet, Kossar Pourahmadi Meibodi, Soroush Abbasi Koohpayegani, Hamed
    Pirsiavash
  year: '2023'
  abstract: 3D Gaussian Splatting is a new method for modeling and rendering 3D radiance
    fields that achieves much faster learning and rendering time compared to SOTA
    NeRF methods. However, it comes with a drawback in the much larger storage demand
    compared to NeRF methods since it needs to store the parameters for several 3D
    Gaussians. We notice that many Gaussians may share similar parameters, so we introduce
    a simple vector quantization method based on \kmeans algorithm to quantize the
    Gaussian parameters. Then, we store the small codebook along with the index of
    the code for each Gaussian. Moreover, we compress the indices further by sorting
    them and using a method similar to run-length encoding. We do extensive experiments
    on standard benchmarks as well as a new benchmark which is an order of magnitude
    larger than the standard benchmarks. We show that our simple yet effective method
    can reduce the storage cost for the original 3D Gaussian Splatting method by a
    factor of almost 20× with a very small drop in the quality of rendered images.
  project_page: https://ucdvision.github.io/compact3d/
  paper: https://arxiv.org/pdf/2311.18159.pdf
  code: https://github.com/UCDvision/compact3d
  video: null
  tags:
  - Code
  - Compression
  - Project
  thumbnail: assets/thumbnails/navaneet2023compact3d.jpg
  publication_date: '2023-11-30T00:29:13+00:00'
- id: kocabas2023hugs
  title: 'HUGS: Human Gaussian Splats'
  authors: Muhammed Kocabas, Jen-Hao Rick Chang, James Gabriel, Oncel Tuzel, Anurag
    Ranjan
  year: '2023'
  abstract: Recent advances in neural rendering have improved both training and rendering
    times by orders of magnitude. While these methods demonstrate state-of-the-art
    quality and speed, they are designed for photogrammetry of static scenes and do
    not generalize well to freely moving humans in the environment. In this work,
    we introduce Human Gaussian Splats (HUGS) that represents an animatable human
    together with the scene using 3D Gaussian Splatting (3DGS). Our method takes only
    a monocular video with a small number of (50-100) frames, and it automatically
    learns to disentangle the static scene and a fully animatable human avatar within
    30 minutes. We utilize the SMPL body model to initialize the human Gaussians.
    To capture details that are not modeled by SMPL (e.g. cloth, hairs), we allow
    the 3D Gaussians to deviate from the human body model. Utilizing 3D Gaussians
    for animated humans brings new challenges, including the artifacts created when
    articulating the Gaussians. We propose to jointly optimize the linear blend skinning
    weights to coordinate the movements of individual Gaussians during animation.
    Our approach enables novel-pose synthesis of human and novel view synthesis of
    both the human and the scene. We achieve state-of-the-art rendering quality with
    a rendering speed of 60 FPS while being ~100x faster to train over previous work.
  project_page: https://machinelearning.apple.com/research/hugs
  paper: https://arxiv.org/pdf/2311.17910.pdf
  code: https://github.com/apple/ml-hugs
  video: null
  tags:
  - Avatar
  - Code
  - Project
  thumbnail: assets/thumbnails/kocabas2023hugs.jpg
  publication_date: '2023-11-29T18:56:32+00:00'
- id: vilesov2023cg3d
  title: 'CG3D: Compositional Generation for Text-to-3D'
  authors: Alexander Vilesov, Pradyumna Chari, Achuta Kadambi
  year: '2023'
  abstract: 'With the onset of diffusion-based generative models and their ability
    to generate text-conditioned images, content generation has received a massive
    invigoration. Recently, these models have been shown to provide useful guidance
    for the generation of 3D graphics assets. However, existing work in text-conditioned
    3D generation faces fundamental constraints: (i) inability to generate detailed,
    multi-object scenes, (ii) inability to textually control multi-object configurations,
    and (iii) physically realistic scene composition. In this work, we propose CG3D,
    a method for compositionally generating scalable 3D assets that resolves these
    constraints. We find that explicit Gaussian radiance fields, parameterized to
    allow for compositions of objects, possess the capability to enable semantically
    and physically consistent scenes. By utilizing a guidance framework built around
    this explicit representation, we show state of the art results, capable of even
    exceeding the guiding diffusion model in terms of object combinations and physics
    accuracy.'
  project_page: https://asvilesov.github.io/CG3D/
  paper: https://arxiv.org/pdf/2311.17907.pdf
  code: null
  video: https://www.youtube.com/watch?v=FMAVeolsE7s
  tags:
  - Diffusion
  - Project
  - Video
  thumbnail: assets/thumbnails/vilesov2023cg3d.jpg
  publication_date: '2023-11-29T18:55:38+00:00'
- id: jiang2023fisherrf
  title: 'FisherRF: Active View Selection and Uncertainty Quantification for Radiance
    Fields using Fisher Information'
  authors: Wen Jiang, Boshu Lei, Kostas Daniilidis
  year: '2023'
  abstract: This study addresses the challenging problem of active view selection
    and uncertainty quantification within the domain of Radiance Fields. Neural Radiance
    Fields (NeRF) have greatly advanced image rendering and reconstruction, but the
    limited availability of 2D images poses uncertainties stemming from occlusions,
    depth ambiguities, and imaging errors. Efficiently selecting informative views
    becomes crucial, and quantifying NeRF model uncertainty presents intricate challenges.
    Existing approaches either depend on model architecture or are based on assumptions
    regarding density distributions that are not generally applicable. By leveraging
    Fisher Information, we efficiently quantify observed information within Radiance
    Fields without ground truth data. This can be used for the next best view selection
    and pixel-wise uncertainty quantification. Our method overcomes existing limitations
    on model architecture and effectiveness, achieving state-of-the-art results in
    both view selection and uncertainty quantification, demonstrating its potential
    to advance the field of Radiance Fields. Our method with the 3D Gaussian Splatting
    backend could perform view selections at 70 fps.
  project_page: https://jiangwenpl.github.io/FisherRF/
  paper: https://arxiv.org/pdf/2311.17874.pdf
  code: https://github.com/JiangWenPL/FisherRF
  video: null
  tags:
  - Code
  - Misc
  - Project
  - Uncertainty
  thumbnail: assets/thumbnails/jiang2023fisherrf.jpg
  publication_date: '2023-11-29T18:20:16+00:00'
- id: abdal2023gaussian
  title: Gaussian Shell Maps for Efficient 3D Human Generation
  authors: Rameen Abdal, Wang Yifan, Zifan Shi, Yinghao Xu, Ryan Po, Zhengfei Kuang,
    Qifeng Chen, Dit-Yan Yeung, Gordon Wetzstein
  year: '2023'
  abstract: Efficient generation of 3D digital humans is important in several industries,
    including virtual reality, social media, and cinematic production. 3D generative
    adversarial networks (GANs) have demonstrated state-of-the-art (SOTA) quality
    and diversity for generated assets. Current 3D GAN architectures, however, typically
    rely on volume representations, which are slow to render, thereby hampering the
    GAN training and requiring multi-view-inconsistent 2D upsamplers. Here, we introduce
    Gaussian Shell Maps (GSMs) as a framework that connects SOTA generator network
    architectures with emerging 3D Gaussian rendering primitives using an articulable
    multi shell–based scaffold. In this setting, a CNN generates a 3D texture stack
    with features that are mapped to the shells. The latter represent inflated and
    deflated versions of a template surface of a digital human in a canonical body
    pose. Instead of rasterizing the shells directly, we sample 3D Gaussians on the
    shells whose attributes are encoded in the texture features. These Gaussians are
    efficiently and differentiably rendered. The ability to articulate the shells
    is important during GAN training and, at inference time, to deform a body into
    arbitrary userdefined poses. Our efficient rendering scheme bypasses the need
    for view-inconsistent upsamplers and achieves highquality multi-view consistent
    renderings at a native resolution of 512 × 512 pixels. We demonstrate that GSMs
    successfully generate 3D humans when trained on single-view datasets, including
    SHHQ and DeepFashion.
  project_page: https://rameenabdal.github.io/GaussianShellMaps/
  paper: https://arxiv.org/pdf/2311.17857
  code: https://github.com/computational-imaging/GSM
  video: null
  tags:
  - Avatar
  - Code
  - Project
  thumbnail: assets/thumbnails/abdal2023gaussian.jpg
  publication_date: '2023-11-29T18:04:07+00:00'
- id: jiang2023gaussianshader
  title: 'GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective
    Surfaces'
  authors: Yingwenqi Jiang, Jiadong Tu, Yuan Liu, Xifeng Gao, Xiaoxiao Long, Wenping
    Wang, Yuexin Ma
  year: '2023'
  abstract: The advent of neural 3D Gaussians has recently brought about a revolution
    in the field of neural rendering, facilitating the generation of high-quality
    renderings at real-time speeds. However, the explicit and discrete representation
    encounters challenges when applied to scenes featuring reflective surfaces. In
    this paper, we present GaussianShader, a novel method that applies a simplified
    shading function on 3D Gaussians to enhance the neural rendering in scenes with
    reflective surfaces while preserving the training and rendering efficiency. The
    main challenge in applying the shading function lies in the accurate normal estimation
    on discrete 3D Gaussians. Specifically, we proposed a novel normal estimation
    framework based on the shortest axis directions of 3D Gaussians with a delicately
    designed loss to make the consistency between the normals and the geometries of
    Gaussian spheres. Experiments show that GaussianShader strikes a commendable balance
    between efficiency and visual quality. Our method surpasses Gaussian Splatting
    in PSNR on specular object datasets, exhibiting an improvement of 1.57dB. When
    compared to prior works handling reflective surfaces, such as Ref-NeRF, our optimization
    time is significantly accelerated (23h vs. 0.58h). Please click on our project
    website to see more results.
  project_page: https://asparagus15.github.io/GaussianShader.github.io/
  paper: https://arxiv.org/pdf/2311.17977.pdf
  code: https://github.com/Asparagus15/GaussianShader
  video: null
  tags:
  - Code
  - Project
  - Relight
  - Rendering
  thumbnail: assets/thumbnails/jiang2023gaussianshader.jpg
  publication_date: '2023-11-29T17:22:26+00:00'
- id: fan2023lightgaussian
  title: 'LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and
    200+ FPS'
  authors: Zhiwen Fan, Kevin Wang, Kairun Wen, Zehao Zhu, Dejia Xu, Zhangyang Wang
  year: '2023'
  abstract: Recent advancements in real-time neural rendering using point-based techniques
    have paved the way for the widespread adoption of 3D representations. However,
    foundational approaches like 3D Gaussian Splatting come with a substantial storage
    overhead caused by growing the SfM points to millions, often demanding gigabyte-level
    disk space for a single unbounded scene, posing significant scalability challenges
    and hindering the splatting efficiency. To address this challenge, we introduce
    LightGaussian, a novel method designed to transform 3D Gaussians into a more efficient
    and compact format. Drawing inspiration from the concept of Network Pruning, LightGaussian
    identifies Gaussians that are insignificant in contributing to the scene reconstruction
    and adopts a pruning and recovery process, effectively reducing redundancy in
    Gaussian counts while preserving visual effects. Additionally, LightGaussian employs
    distillation and pseudo-view augmentation to distill spherical harmonics to a
    lower degree, allowing knowledge transfer to more compact representations while
    maintaining reflectance. Furthermore, we propose a hybrid scheme, VecTree Quantization,
    to quantize all attributes, resulting in lower bitwidth representations with minimal
    accuracy losses. In summary, LightGaussian achieves an averaged compression rate
    over 15x while boosting the FPS from 139 to 215, enabling an efficient representation
    of complex scenes on Mip-NeRF 360, Tank and Temple datasets.
  project_page: https://lightgaussian.github.io/
  paper: https://arxiv.org/pdf/2311.17245.pdf
  code: https://github.com/VITA-Group/LightGaussian
  video: https://www.youtube.com/watch?v=470hul75bSM
  tags:
  - Code
  - Compression
  - Project
  - Video
  thumbnail: assets/thumbnails/fan2023lightgaussian.jpg
  publication_date: '2023-11-28T21:39:20+00:00'
- id: liu2023humangaussian
  title: 'HumanGaussian: Text-Driven 3D Human Generation with Gaussian Splatting'
  authors: Xian Liu, Xiaohang Zhan, Jiaxiang Tang, Ying Shan, Gang Zeng, Dahua Lin,
    Xihui Liu, Ziwei Liu
  year: '2023'
  abstract: Realistic 3D human generation from text prompts is a desirable yet challenging
    task. Existing methods optimize 3D representations like mesh or neural fields
    via score distillation sampling (SDS), which suffers from inadequate fine details
    or excessive training time. In this paper, we propose an efficient yet effective
    framework, HumanGaussian, that generates high-quality 3D humans with fine-grained
    geometry and realistic appearance. Our key insight is that 3D Gaussian Splatting
    is an efficient renderer with periodic Gaussian shrinkage or growing, where such
    adaptive density control can be naturally guided by intrinsic human structures.
    Specifically, 1) we first propose a Structure-Aware SDS that simultaneously optimizes
    human appearance and geometry. The multi-modal score function from both RGB and
    depth space is leveraged to distill the Gaussian densification and pruning process.
    2) Moreover, we devise an Annealed Negative Prompt Guidance by decomposing SDS
    into a noisier generative score and a cleaner classifier score, which well addresses
    the over-saturation issue. The floating artifacts are further eliminated based
    on Gaussian size in a prune-only phase to enhance generation smoothness. Extensive
    experiments demonstrate the superior efficiency and competitive quality of our
    framework, rendering vivid 3D humans under diverse scenarios.
  project_page: https://alvinliu0.github.io/projects/HumanGaussian
  paper: https://arxiv.org/pdf/2311.17061.pdf
  code: https://github.com/alvinliu0/HumanGaussian
  video: https://www.youtube.com/watch?v=S3djzHoqPKY
  tags:
  - Avatar
  - Code
  - Diffusion
  - Project
  - Video
  thumbnail: assets/thumbnails/liu2023humangaussian.jpg
  publication_date: '2023-11-28T18:59:58+00:00'
- id: huang2023pointn
  title: 'Point''n Move: Interactive Scene Object Manipulation on Gaussian Splatting
    Radiance Fields'
  authors: Jiajun Huang, Hongchuan Yu
  year: '2023'
  abstract: We propose Point'n Move, a method that achieves interactive scene object
    manipulation with exposed region inpainting. Interactivity here further comes
    from intuitive object selection and real-time editing. To achieve this, we adopt
    Gaussian Splatting Radiance Field as the scene representation and fully leverage
    its explicit nature and speed advantage. Its explicit representation formulation
    allows us to devise a 2D prompt points to 3D mask dual-stage self-prompting segmentation
    algorithm, perform mask refinement and merging, minimize change as well as provide
    good initialization for scene inpainting and perform editing in real-time without
    per-editing training, all leads to superior quality and performance. We test our
    method by performing editing on both forward-facing and 360 scenes. We also compare
    our method against existing scene object removal methods, showing superior quality
    despite being more capable and having a speed advantage.
  project_page: null
  paper: https://arxiv.org/pdf/2311.16737.pdf
  code: null
  video: null
  tags:
  - Editing
  thumbnail: assets/thumbnails/huang2023pointn.jpg
  publication_date: '2023-11-28T12:33:49+00:00'
- id: huang2023photoslam
  title: 'Photo-SLAM: Real-time Simultaneous Localization and Photorealistic Mapping
    for Monocular, Stereo, and RGB-D Cameras'
  authors: Huajian Huang, Longwei Li, Hui Cheng, Sai-Kit Yeung
  year: '2023'
  abstract: The integration of neural rendering and the SLAM system recently showed
    promising results in joint localization and photorealistic view reconstruction.
    However, existing methods, fully relying on implicit representations, are so resource-hungry
    that they cannot run on portable devices, which deviates from the original intention
    of SLAM. In this paper, we present Photo-SLAM, a novel SLAM framework with a hyper
    primitives map. Specifically, we simultaneously exploit explicit geometric features
    for localization and learn implicit photometric features to represent the texture
    information of the observed environment. In addition to actively densifying hyper
    primitives based on geometric features, we further introduce a Gaussian-Pyramid-based
    training method to progressively learn multi-level features, enhancing photorealistic
    mapping performance. The extensive experiments with monocular, stereo, and RGB-D
    datasets prove that our proposed system Photo-SLAM significantly outperforms current
    state-of-the-art SLAM systems for online photorealistic mapping, e.g., PSNR is
    30% higher and rendering speed is hundreds of times faster in the Replica dataset.
    Moreover, the Photo-SLAM can run at real-time speed using an embedded platform
    such as Jetson AGX Orin, showing the potential of robotics applications.
  project_page: https://huajianup.github.io/research/Photo-SLAM/
  paper: https://arxiv.org/pdf/2311.16728.pdf
  code: https://github.com/HuajianUP/Photo-SLAM
  video: https://www.youtube.com/watch?v=WKXCFNJHZ4o
  tags:
  - Code
  - Project
  - SLAM
  - Video
  thumbnail: assets/thumbnails/huang2023photoslam.jpg
  publication_date: '2023-11-28T12:19:00+00:00'
- id: moreau2023human
  title: 'Human Gaussian Splatting: Real-time Rendering of Animatable Avatars'
  authors: Arthur Moreau, Jifei Song, Helisa Dhamo, Richard Shaw, Yiren Zhou, Eduardo
    Pérez-Pellitero
  year: '2023'
  abstract: This work addresses the problem of real-time rendering of photorealistic
    human body avatars learned from multi-view videos. While the classical approaches
    to model and render virtual humans generally use a textured mesh, recent research
    has developed neural body representations that achieve impressive visual quality.
    However, these models are difficult to render in real-time and their quality degrades
    when the character is animated with body poses different than the training observations.
    We propose an animatable human model based on 3D Gaussian Splatting, that has
    recently emerged as a very efficient alternative to neural radiance fields. The
    body is represented by a set of gaussian primitives in a canonical space which
    is deformed with a coarse to fine approach that combines forward skinning and
    local non-rigid refinement. We describe how to learn our Human Gaussian Splatting
    (HuGS) model in an end-to-end fashion from multi-view observations, and evaluate
    it against the state-of-the-art approaches for novel pose synthesis of clothed
    body. Our method achieves 1.5 dB PSNR improvement over the state-of-the-art on
    THuman4 dataset while being able to render in real-time (80 fps for 512x512 resolution).
  project_page: https://perezpellitero.github.io/projects/hugs/index.html
  paper: https://arxiv.org/pdf/2311.17113.pdf
  code: null
  video: https://www.youtube.com/watch?v=R3CHg46SIfo
  tags:
  - Avatar
  - Project
  - Video
  thumbnail: assets/thumbnails/moreau2023human.jpg
  publication_date: '2023-11-28T12:05:41+00:00'
- id: yan2023multiscale
  title: Multi-Scale 3D Gaussian Splatting for Anti-Aliased Rendering
  authors: Zhiwen Yan, Weng Fei Low, Yu Chen, Gim Hee Lee
  year: '2023'
  abstract: 3D Gaussians have recently emerged as a highly efficient representation
    for 3D reconstruction and rendering. Despite its high rendering quality and speed
    at high resolutions, they both deteriorate drastically when rendered at lower
    resolutions or from far away camera position. During low resolution or far away
    rendering, the pixel size of the image can fall below the Nyquist frequency compared
    to the screen size of each splatted 3D Gaussian and leads to aliasing effect.
    The rendering is also drastically slowed down by the sequential alpha blending
    of more splatted Gaussians per pixel. To address these issues, we propose a multi-scale
    3D Gaussian splatting algorithm, which maintains Gaussians at different scales
    to represent the same scene. Higher-resolution images are rendered with more small
    Gaussians, and lower-resolution images are rendered with fewer larger Gaussians.
    With similar training time, our algorithm can achieve 13\%-66\% PSNR and 160\%-2400\%
    rendering speed improvement at 4×-128× scale rendering on Mip-NeRF360 dataset
    compared to the single scale 3D Gaussian splatting.
  project_page: null
  paper: https://arxiv.org/pdf/2311.17089.pdf
  code: null
  video: null
  tags:
  - Antialiasing
  - Rendering
  thumbnail: assets/thumbnails/yan2023multiscale.jpg
  publication_date: '2023-11-28T03:31:35+00:00'
- id: lei2023gart
  title: 'GART: Gaussian Articulated Template Models'
  authors: Jiahui Lei, Yufu Wang, Georgios Pavlakos, Lingjie Liu, Kostas Daniilidis
  year: '2023'
  abstract: We introduce Gaussian Articulated Template Model GART, an explicit, efficient,
    and expressive representation for non-rigid articulated subject capturing and
    rendering from monocular videos. GART utilizes a mixture of moving 3D Gaussians
    to explicitly approximate a deformable subject's geometry and appearance. It takes
    advantage of a categorical template model prior (SMPL, SMAL, etc.) with learnable
    forward skinning while further generalizing to more complex non-rigid deformations
    with novel latent bones. GART can be reconstructed via differentiable rendering
    from monocular videos in seconds or minutes and rendered in novel poses faster
    than 150fps.
  project_page: https://www.cis.upenn.edu/~leijh/projects/gart/
  paper: https://arxiv.org/pdf/2311.16099.pdf
  code: https://github.com/JiahuiLei/GART
  video: https://www.youtube.com/watch?v=-xYNtIlW4WY
  tags:
  - Avatar
  thumbnail: assets/thumbnails/lei2023gart.jpg
  publication_date: '2023-11-27T18:59:30+00:00'
- id: li2023animatable
  title: 'Animatable Gaussians: Learning Pose-dependent Gaussian Maps for High-fidelity
    Human Avatar Modeling'
  authors: Zhe Li, Zerong Zheng, Lizhen Wang, Yebin Liu
  year: '2023'
  abstract: Modeling animatable human avatars from RGB videos is a long-standing and
    challenging problem. Recent works usually adopt MLP-based neural radiance fields
    (NeRF) to represent 3D humans, but it remains difficult for pure MLPs to regress
    pose-dependent garment details. To this end, we introduce Animatable Gaussians,
    a new avatar representation that leverages powerful 2D CNNs and 3D Gaussian splatting
    to create high-fidelity avatars. To associate 3D Gaussians with the animatable
    avatar, we learn a parametric template from the input videos, and then parameterize
    the template on two front & back canonical Gaussian maps where each pixel represents
    a 3D Gaussian. The learned template is adaptive to the wearing garments for modeling
    looser clothes like dresses. Such template-guided 2D parameterization enables
    us to employ a powerful StyleGAN-based CNN to learn the pose-dependent Gaussian
    maps for modeling detailed dynamic appearances. Furthermore, we introduce a pose
    projection strategy for better generalization given novel poses. Overall, our
    method can create lifelike avatars with dynamic, realistic and generalized appearances.
    Experiments show that our method outperforms other state-of-the-art approaches.
  project_page: https://animatable-gaussians.github.io/
  paper: https://arxiv.org/pdf/2311.16096.pdf
  code: https://github.com/lizhe00/AnimatableGaussians
  video: https://www.youtube.com/watch?v=kOmZxD0HxZI
  tags:
  - Avatar
  - Code
  - Project
  - Video
  thumbnail: assets/thumbnails/li2023animatable.jpg
  publication_date: '2023-11-27T18:59:04+00:00'
- id: gao2023relightable
  title: 'Relightable 3D Gaussian: Real-time Point Cloud Relighting with BRDF Decomposition
    and Ray Tracing'
  authors: Jian Gao, Chun Gu, Youtian Lin, Hao Zhu, Xun Cao, Li Zhang, Yao Yao
  year: '2023'
  abstract: We present a novel differentiable point-based rendering framework for
    material and lighting decomposition from multi-view images, enabling editing,
    ray-tracing, and real-time relighting of the 3D point cloud. Specifically, a 3D
    scene is represented as a set of relightable 3D Gaussian points, where each point
    is additionally associated with a normal direction, BRDF parameters, and incident
    lights from different directions. To achieve robust lighting estimation, we further
    divide incident lights of each point into global and local components, as well
    as view-dependent visibilities. The 3D scene is optimized through the 3D Gaussian
    Splatting technique while BRDF and lighting are decomposed by physically-based
    differentiable rendering. Moreover, we introduce an innovative point-based ray-tracing
    approach based on the bounding volume hierarchy for efficient visibility baking,
    enabling real-time rendering and relighting of 3D Gaussian points with accurate
    shadow effects. Extensive experiments demonstrate improved BRDF estimation and
    novel view rendering results compared to state-of-the-art material estimation
    approaches. Our framework showcases the potential to revolutionize the mesh-based
    graphics pipeline with a relightable, traceable, and editable rendering pipeline
    solely based on point cloud.
  project_page: https://nju-3dv.github.io/projects/Relightable3DGaussian/
  paper: https://arxiv.org/pdf/2311.16043.pdf
  code: https://github.com/NJU-3DV/Relightable3DGaussian
  video: null
  tags:
  - Code
  - Project
  - Ray Tracing
  - Relight
  - Rendering
  thumbnail: assets/thumbnails/gao2023relightable.jpg
  publication_date: '2023-11-27T18:07:58+00:00'
- id: fang2023gaussianeditor
  title: 'GaussianEditor: Editing 3D Gaussians Delicately with Text Instructions'
  authors: Jiemin Fang, Junjie Wang, Xiaopeng Zhang, Lingxi Xie, Qi Tian
  year: '2023'
  abstract: Recently, impressive results have been achieved in 3D scene editing with
    text instructions based on a 2D diffusion model. However, current diffusion models
    primarily generate images by predicting noise in the latent space, and the editing
    is usually applied to the whole image, which makes it challenging to perform delicate,
    especially localized, editing for 3D scenes. Inspired by recent 3D Gaussian splatting,
    we propose a systematic framework, named GaussianEditor, to edit 3D scenes delicately
    via 3D Gaussians with text instructions. Benefiting from the explicit property
    of 3D Gaussians, we design a series of techniques to achieve delicate editing.
    Specifically, we first extract the region of interest (RoI) corresponding to the
    text instruction, aligning it to 3D Gaussians. The Gaussian RoI is further used
    to control the editing process. Our framework can achieve more delicate and precise
    editing of 3D scenes than previous methods while enjoying much faster training
    speed, i.e. within 20 minutes on a single V100 GPU, more than twice as fast as
    Instruct-NeRF2NeRF (45 minutes -- 2 hours)
  project_page: https://gaussianeditor.github.io/
  paper: https://arxiv.org/pdf/2311.16037.pdf
  code: null
  video: https://youtu.be/KWtALsigR3k?si=h6-A44brd5rm3_CM
  tags:
  - Editing
  - Project
  - Video
  thumbnail: assets/thumbnails/fang2023gaussianeditor.jpg
  publication_date: '2023-11-27T17:58:21+00:00'
- id: yu2023mipsplatting
  title: Mip-Splatting Alias-free 3D Gaussian Splatting
  authors: Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, Andreas Geiger
  year: '2023'
  abstract: Recently, 3D Gaussian Splatting (3DGS) has demonstrated impressive novel
    view synthesis results, reaching high fidelity and efficiency. However, strong
    artifacts can be observed when changing the sampling rate, e.g., by changing focal
    length or camera distance. We find that the source for this phenomenon can be
    attributed to the lack of 3D frequency constraints and the usage of a 2D dilation
    filter. To address this problem, we introduce a 3D smoothing filter which constrains
    the size of the 3D Gaussian primitives based on the maximal sampling frequency
    induced by the input views, eliminating high frequency artifacts when zooming
    in. Moreover, replacing 2D dilation with a 2D Mip filter, which simulates a 2D
    box filter, effectively mitigates aliasing and dilation issues. Our comprehensive
    evaluation, including scenarios such as training on single-scale images and testing
    on multiple scales, validates the effectiveness of our approach.
  project_page: https://niujinshuchong.github.io/mip-splatting/
  paper: https://arxiv.org/pdf/2311.16493.pdf
  code: https://github.com/autonomousvision/mip-splatting
  video: null
  tags:
  - Antialiasing
  - Code
  - Project
  - Rendering
  thumbnail: assets/thumbnails/yu2023mipsplatting.jpg
  publication_date: '2023-11-27T13:03:09+00:00'
- id: liang2023gsir
  title: 'GS-IR: 3D Gaussian Splatting for Inverse Rendering'
  authors: Zhihao Liang, Qi Zhang, Ying Feng, Ying Shan, Kui Jia
  year: '2023'
  abstract: 'We propose GS-IR, a novel inverse rendering approach based on 3D Gaussian
    Splatting (GS) that leverages forward mapping volume rendering to achieve photorealistic
    novel view synthesis and relighting results. Unlike previous works that use implicit
    neural representations and volume rendering (e.g. NeRF), which suffer from low
    expressive power and high computational complexity, we extend GS, a top-performance
    representation for novel view synthesis, to estimate scene geometry, surface material,
    and environment illumination from multi-view images captured under unknown lighting
    conditions. There are two main problems when introducing GS to inverse rendering:
    1) GS does not support producing plausible normal natively; 2) forward mapping
    (e.g. rasterization and splatting) cannot trace the occlusion like backward mapping
    (e.g. ray tracing). To address these challenges, our GS-IR proposes an efficient
    optimization scheme that incorporates a depth-derivation-based regularization
    for normal estimation and a baking-based occlusion to model indirect lighting.
    The flexible and expressive GS representation allows us to achieve fast and compact
    geometry reconstruction, photorealistic novel view synthesis, and effective physically-based
    rendering. We demonstrate the superiority of our method over baseline methods
    through qualitative and quantitative evaluations on various challenging scenes.'
  project_page: https://github.com/lzhnb/GS-IR
  paper: https://arxiv.org/pdf/2311.16473.pdf
  code: https://github.com/lzhnb/GS-IR
  video: null
  tags:
  - Code
  - Project
  - Relight
  - Rendering
  thumbnail: assets/thumbnails/liang2023gsir.jpg
  publication_date: '2023-11-26T02:35:09+00:00'
- id: chen2023gaussianeditor
  title: 'GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting'
  authors: Yiwen Chen, Zilong Chen, Chi Zhang, Feng Wang, Xiaofeng Yang, Yikai Wang,
    Zhongang Cai, Lei Yang, Huaping Liu, Guosheng Lin
  year: '2023'
  abstract: 3D editing plays a crucial role in many areas such as gaming and virtual
    reality. Traditional 3D editing methods, which rely on representations like meshes
    and point clouds, often fall short in realistically depicting complex scenes.
    On the other hand, methods based on implicit 3D representations, like Neural Radiance
    Field (NeRF), render complex scenes effectively but suffer from slow processing
    speeds and limited control over specific scene areas. In response to these challenges,
    our paper presents GaussianEditor, an innovative and efficient 3D editing algorithm
    based on Gaussian Splatting (GS), a novel 3D representation technique. GaussianEditor
    enhances precision and control in editing through our proposed Gaussian Semantic
    Tracing, which traces the editing target throughout the training process. Additionally,
    we propose hierarchical Gaussian splatting (HGS) to achieve stabilized and fine
    results under stochastic generative guidance from 2D diffusion models. We also
    develop editing strategies for efficient object removal and integration, a challenging
    task for existing methods. Our comprehensive experiments demonstrate GaussianEditor's
    superior control, efficacy, and rapid performance, marking a significant advancement
    in 3D editing.
  project_page: https://buaacyw.github.io/gaussian-editor/
  paper: https://arxiv.org/pdf/2311.14521.pdf
  code: https://github.com/buaacyw/GaussianEditor
  video: https://youtu.be/TdZIICSFqsU?si=-U4tyOvaAPqIROYn
  tags:
  - Code
  - Editing
  - Project
  - Video
  thumbnail: assets/thumbnails/chen2023gaussianeditor.jpg
  publication_date: '2023-11-24T14:46:59+00:00'
- id: chan2023compact
  title: Compact 3D Gaussian Representation for Radiance Field
  authors: Joo Chan Lee, Daniel Rho, Xiangyu Sun, Jong Hwan Ko, Eunbyung Park
  year: '2023'
  abstract: 'Neural Radiance Fields (NeRFs) have demonstrated remarkable potential
    in capturing complex 3D scenes with high fidelity. However, one persistent challenge
    that hinders the widespread adoption of NeRFs is the computational bottleneck
    due to the volumetric rendering. On the other hand, 3D Gaussian splatting (3DGS)
    has recently emerged as an alternative representation that leverages a 3D Gaussisan-based
    representation and adopts the rasterization pipeline to render the images rather
    than volumetric rendering, achieving very fast rendering speed and promising image
    quality. However, a significant drawback arises as 3DGS entails a substantial
    number of 3D Gaussians to maintain the high fidelity of the rendered images, which
    requires a large amount of memory and storage. To address this critical issue,
    we place a specific emphasis on two key objectives: reducing the number of Gaussian
    points without sacrificing performance and compressing the Gaussian attributes,
    such as view-dependent color and covariance. To this end, we propose a learnable
    mask strategy that significantly reduces the number of Gaussians while preserving
    high performance. In addition, we propose a compact but effective representation
    of view-dependent color by employing a grid-based neural field rather than relying
    on spherical harmonics. Finally, we learn codebooks to compactly represent the
    geometric attributes of Gaussian by vector quantization. In our extensive experiments,
    we consistently show over 10× reduced storage and enhanced rendering speed, while
    maintaining the quality of the scene representation, compared to 3DGS. Our work
    provides a comprehensive framework for 3D scene representation, achieving high
    performance, fast training, compactness, and real-time rendering.'
  project_page: https://maincold2.github.io/c3dgs/
  paper: https://arxiv.org/pdf/2311.13681.pdf
  code: https://github.com/maincold2/Compact-3DGS
  video: null
  tags:
  - Code
  - Compression
  - Project
  thumbnail: assets/thumbnails/chan2023compact.jpg
  publication_date: '2023-11-22T20:31:16+00:00'
- id: chung2023depthregularized
  title: Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images
  authors: Jaeyoung Chung, Jeongtaek Oh, Kyoung Mu Lee
  year: '2023'
  abstract: In this paper, we present a method to optimize Gaussian splatting with
    a limited number of images while avoiding overfitting. Representing a 3D scene
    by combining numerous Gaussian splats has yielded outstanding visual quality.
    However, it tends to overfit the training views when only a small number of images
    are available. To address this issue, we introduce a dense depth map as a geometry
    guide to mitigate overfitting. We obtained the depth map using a pre-trained monocular
    depth estimation model and aligning the scale and offset using sparse COLMAP feature
    points. The adjusted depth aids in the color-based optimization of 3D Gaussian
    splatting, mitigating floating artifacts, and ensuring adherence to geometric
    constraints. We verify the proposed method on the NeRF-LLFF dataset with varying
    numbers of few images. Our approach demonstrates robust geometry compared to the
    original method that relies solely on images.
  project_page: https://robot0321.github.io/DepthRegGS/index.html
  paper: https://arxiv.org/pdf/2311.13398.pdf
  code: https://github.com/robot0321/DepthRegularizedGS
  video: null
  tags:
  - Code
  - Project
  - Sparse
  thumbnail: assets/thumbnails/chung2023depthregularized.jpg
  publication_date: '2023-11-22T13:53:04+00:00'
- id: chung2023luciddreamer
  title: 'LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes'
  authors: Jaeyoung Chung, Suyoung Lee, Hyeongjin Nam, Jaerin Lee, Kyoung Mu Lee
  year: '2023'
  abstract: 'With the widespread usage of VR devices and contents, demands for 3D
    scene generation techniques become more popular. Existing 3D scene generation
    models, however, limit the target scene to specific domain, primarily due to their
    training strategies using 3D scan dataset that is far from the real-world. To
    address such limitation, we propose LucidDreamer, a domain-free scene generation
    pipeline by fully leveraging the power of existing large-scale diffusion-based
    generative model. Our LucidDreamer has two alternate steps: Dreaming and Alignment.
    First, to generate multi-view consistent images from inputs, we set the point
    cloud as a geometrical guideline for each image generation. Specifically, we project
    a portion of point cloud to the desired view and provide the projection as a guidance
    for inpainting using the generative model. The inpainted images are lifted to
    3D space with estimated depth maps, composing a new points. Second, to aggregate
    the new points into the 3D scene, we propose an aligning algorithm which harmoniously
    integrates the portions of newly generated 3D scenes. The finally obtained 3D
    scene serves as initial points for optimizing Gaussian splats. LucidDreamer produces
    Gaussian splats that are highly-detailed compared to the previous 3D scene generation
    methods, with no constraint on domain of the target scene.'
  project_page: https://luciddreamer-cvlab.github.io/
  paper: https://arxiv.org/pdf/2311.13384.pdf
  code: https://github.com/luciddreamer-cvlab/LucidDreamer?tab=readme-ov-file
  video: null
  tags:
  - Code
  - Diffusion
  - Project
  - World Generation
  thumbnail: assets/thumbnails/chung2023luciddreamer.jpg
  publication_date: '2023-11-22T13:27:34+00:00'
- id: guédon2023sugar
  title: 'SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction
    and High-Quality Mesh Rendering'
  authors: Antoine Guédon, Vincent Lepetit
  year: '2023'
  abstract: We propose a method to allow precise and extremely fast mesh extraction
    from 3D Gaussian Splatting. Gaussian Splatting has recently become very popular
    as it yields realistic rendering while being significantly faster to train than
    NeRFs. It is however challenging to extract a mesh from the millions of tiny 3D
    gaussians as these gaussians tend to be unorganized after optimization and no
    method has been proposed so far. Our first key contribution is a regularization
    term that encourages the gaussians to align well with the surface of the scene.
    We then introduce a method that exploits this alignment to sample points on the
    real surface of the scene and extract a mesh from the Gaussians using Poisson
    reconstruction, which is fast, scalable, and preserves details, in contrast to
    the Marching Cubes algorithm usually applied to extract meshes from Neural SDFs.
    Finally, we introduce an optional refinement strategy that binds gaussians to
    the surface of the mesh, and jointly optimizes these Gaussians and the mesh through
    Gaussian splatting rendering. This enables easy editing, sculpting, rigging, animating,
    compositing and relighting of the Gaussians using traditional softwares by manipulating
    the mesh instead of the gaussians themselves. Retrieving such an editable mesh
    for realistic rendering is done within minutes with our method, compared to hours
    with the state-of-the-art methods on neural SDFs, while providing a better rendering
    quality.
  project_page: https://imagine.enpc.fr/~guedona/sugar/
  paper: https://arxiv.org/pdf/2311.12775.pdf
  code: https://github.com/Anttwo/SuGaR
  video: https://www.youtube.com/watch?v=MAkFyWfiBQo.&t
  tags:
  - Code
  - Meshing
  - Project
  - Video
  thumbnail: assets/thumbnails/guédon2023sugar.jpg
  publication_date: '2023-11-21T18:38:03+00:00'
- id: katsumata2023a
  title: A Compact Dynamic 3D Gaussian Representation for Real-Time Dynamic View Synthesis
  authors: Kai Katsumata, Duc Minh Vo, Hideki Nakayama
  year: '2023'
  abstract: In novel view synthesis of scenes from multiple input views, 3D Gaussian
    splatting emerges as a viable alternative to existing radiance field approaches,
    delivering great visual quality and real-time rendering. While successful in static
    scenes, the present advancement of 3D Gaussian representation, however, faces
    challenges in dynamic scenes in terms of memory consumption and the need for numerous
    observations per time step, due to the onus of storing 3D Gaussian parameters
    per time step. In this study, we present an efficient 3D Gaussian representation
    tailored for dynamic scenes in which we define positions and rotations as functions
    of time while leaving other time-invariant properties of the static 3D Gaussian
    unchanged. Notably, our representation reduces memory usage, which is consistent
    regardless of the input sequence length. Additionally, it mitigates the risk of
    overfitting observed frames by accounting for temporal changes. The optimization
    of our Gaussian representation based on image and flow reconstruction results
    in a powerful framework for dynamic scene view synthesis in both monocular and
    multi-view cases. We obtain the highest rendering speed of 118 frames per second
    (FPS) at a resolution of 1352×1014 with a single GPU, showing the practical usability
    and effectiveness of our proposed method in dynamic scene rendering scenarios
  project_page: https://compactdynamic3dgaussian.github.io/
  paper: https://arxiv.org/pdf/2311.12897.pdf
  code: https://github.com/raven38/EfficientDynamic3DGaussian
  video: null
  tags:
  - Code
  - Compression
  - Dynamic
  - Project
  thumbnail: assets/thumbnails/katsumata2023a.jpg
  publication_date: '2023-11-21T09:17:14+00:00'
- id: xie2023physgaussian
  title: 'PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics'
  authors: Tianyi Xie, Zeshun Zong, Yuxin Qiu, Xuan Li, Yutao Feng, Yin Yang, Chenfanfu
    Jiang
  year: '2023'
  abstract: 'We introduce PhysGaussian, a new method that seamlessly integrates physically
    grounded Newtonian dynamics within 3D Gaussians to achieve high-quality novel
    motion synthesis. Employing a custom Material Point Method (MPM), our approach
    enriches 3D Gaussian kernels with physically meaningful kinematic deformation
    and mechanical stress attributes, all evolved in line with continuum mechanics
    principles. A defining characteristic of our method is the seamless integration
    between physical simulation and visual rendering: both components utilize the
    same 3D Gaussian kernels as their discrete representations. This negates the necessity
    for triangle/tetrahedron meshing, marching cubes, "cage meshes," or any other
    geometry embedding, highlighting the principle of "what you see is what you simulate
    (WS2)." Our method demonstrates exceptional versatility across a wide variety
    of materials--including elastic entities, metals, non-Newtonian fluids, and granular
    materials--showcasing its strong capabilities in creating diverse visual content
    with novel viewpoints and movements.'
  project_page: https://xpandora.github.io/PhysGaussian/
  paper: https://arxiv.org/pdf/2311.12198.pdf
  code: https://github.com/XPandora/PhysGaussian
  video: https://www.youtube.com/watch?v=V96GfcMUH2Q
  tags:
  - Code
  - Physics
  - Project
  - Video
  thumbnail: assets/thumbnails/xie2023physgaussian.jpg
  publication_date: '2023-11-20T21:34:52+00:00'
- id: yan2023gsslam
  title: 'GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting'
  authors: Chi Yan, Delin Qu, Dong Wang, Dan Xu, Zhigang Wang, Bin Zhao, Xuelong Li
  year: '2023'
  abstract: In this paper, we introduce GS-SLAM that first utilizes 3D Gaussian representation
    in the Simultaneous Localization and Mapping (SLAM) system. It facilitates a better
    balance between efficiency and accuracy. Compared to recent SLAM methods employing
    neural implicit representations, our method utilizes a real-time differentiable
    splatting rendering pipeline that offers significant speedup to map optimization
    and RGB-D re-rendering. Specifically, we propose an adaptive expansion strategy
    that adds new or deletes noisy 3D Gaussian in order to efficiently reconstruct
    new observed scene geometry and improve the mapping of previously observed areas.
    This strategy is essential to extend 3D Gaussian representation to reconstruct
    the whole scene rather than synthesize a static object in existing methods. Moreover,
    in the pose tracking process, an effective coarse-to-fine technique is designed
    to select reliable 3D Gaussian representations to optimize camera pose, resulting
    in runtime reduction and robust estimation. Our method achieves competitive performance
    compared with existing state-of-the-art real-time methods on the Replica, TUM-RGBD
    datasets. The source code will be released upon acceptance.
  project_page: https://gs-slam.github.io/
  paper: https://arxiv.org/pdf/2311.11700.pdf
  code: https://github.com/yanchi-3dv/diff-gaussian-rasterization-for-gsslam
  video: null
  tags:
  - Code
  - Project
  - SLAM
  thumbnail: assets/thumbnails/yan2023gsslam.jpg
  publication_date: '2023-11-20T12:08:23+00:00'
- id: liang2023luciddreamer
  title: 'LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score
    Matching'
  authors: Yixun Liang, Xin Yang, Jiantao Lin, Haodong Li, Xiaogang Xu, Yingcong Chen
  year: '2023'
  abstract: The recent advancements in text-to-3D generation mark a significant milestone
    in generative models, unlocking new possibilities for creating imaginative 3D
    assets across various real-world scenarios. While recent advancements in text-to-3D
    generation have shown promise, they often fall short in rendering detailed and
    high-quality 3D models. This problem is especially prevalent as many methods base
    themselves on Score Distillation Sampling (SDS). This paper identifies a notable
    deficiency in SDS, that it brings inconsistent and low-quality updating direction
    for the 3D model, causing the over-smoothing effect. To address this, we propose
    a novel approach called Interval Score Matching (ISM). ISM employs deterministic
    diffusing trajectories and utilizes interval-based score matching to counteract
    over-smoothing. Furthermore, we incorporate 3D Gaussian Splatting into our text-to-3D
    generation pipeline. Extensive experiments show that our model largely outperforms
    the state-of-the-art in quality and training efficiency.
  project_page: null
  paper: https://arxiv.org/pdf/2311.11284.pdf
  code: https://github.com/EnVision-Research/LucidDreamer
  video: null
  tags:
  - Code
  - Diffusion
  thumbnail: assets/thumbnails/liang2023luciddreamer.jpg
  publication_date: '2023-11-19T09:59:09+00:00'
- id: li2023gaussiandiffusion
  title: 'GaussianDiffusion: 3D Gaussian Splatting for Denoising Diffusion Probabilistic
    Models with Structured Noise'
  authors: Xinhai Li, Huaibin Wang, Kuo-Kun Tseng
  year: '2023'
  abstract: Text-to-3D, known for its efficient generation methods and expansive creative
    potential, has garnered significant attention in the AIGC domain. However, the
    amalgamation of Nerf and 2D diffusion models frequently yields oversaturated images,
    posing severe limitations on downstream industrial applications due to the constraints
    of pixelwise rendering method. Gaussian splatting has recently superseded the
    traditional pointwise sampling technique prevalent in NeRF-based methodologies,
    revolutionizing various aspects of 3D reconstruction. This paper introduces a
    novel text to 3D content generation framework based on Gaussian splatting, enabling
    fine control over image saturation through individual Gaussian sphere transparencies,
    thereby producing more realistic images. The challenge of achieving multi-view
    consistency in 3D generation significantly impedes modeling complexity and accuracy.
    Taking inspiration from SJC, we explore employing multi-view noise distributions
    to perturb images generated by 3D Gaussian splatting, aiming to rectify inconsistencies
    in multi-view geometry. We ingeniously devise an efficient method to generate
    noise that produces Gaussian noise from diverse viewpoints, all originating from
    a shared noise source. Furthermore, vanilla 3D Gaussian-based generation tends
    to trap models in local minima, causing artifacts like floaters, burrs, or proliferative
    elements. To mitigate these issues, we propose the variational Gaussian splatting
    technique to enhance the quality and stability of 3D appearance. To our knowledge,
    our approach represents the first comprehensive utilization of Gaussian splatting
    across the entire spectrum of 3D content generation processes.
  project_page: null
  paper: https://arxiv.org/pdf/2311.11221.pdf
  code: null
  video: null
  tags:
  - Diffusion
  thumbnail: assets/thumbnails/li2023gaussiandiffusion.jpg
  publication_date: '2023-11-19T04:26:16+00:00'
- id: jena2023splatarmor
  title: 'SplatArmor: Articulated Gaussian splatting for animatable humans from monocular
    RGB videos'
  authors: Rohit Jena, Ganesh Subramanian Iyer, Siddharth Choudhary, Brandon Smith,
    Pratik Chaudhari, James Gee
  year: '2023'
  abstract: We propose SplatArmor, a novel approach for recovering detailed and animatable
    human models by `armoring' a parameterized body model with 3D Gaussians. Our approach
    represents the human as a set of 3D Gaussians within a canonical space, whose
    articulation is defined by extending the skinning of the underlying SMPL geometry
    to arbitrary locations in the canonical space. To account for pose-dependent effects,
    we introduce a SE(3) field, which allows us to capture both the location and anisotropy
    of the Gaussians. Furthermore, we propose the use of a neural color field to provide
    color regularization and 3D supervision for the precise positioning of these Gaussians.
    We show that Gaussian splatting provides an interesting alternative to neural
    rendering based methods by leverging a rasterization primitive without facing
    any of the non-differentiability and optimization challenges typically faced in
    such approaches. The rasterization paradigms allows us to leverage forward skinning,
    and does not suffer from the ambiguities associated with inverse skinning and
    warping. We show compelling results on the ZJU MoCap and People Snapshot datasets,
    which underscore the effectiveness of our method for controllable human synthesis.
  project_page: https://jenaroh.it/splatarmor/
  paper: https://arxiv.org/pdf/2311.10812.pdf
  code: https://github.com/rohitrango/splatarmor
  video: null
  tags:
  - Avatar
  - Code
  - Project
  thumbnail: assets/thumbnails/jena2023splatarmor.jpg
  publication_date: '2023-11-17T18:47:07+00:00'
- id: niedermayr2024compressed
  title: Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis
  authors: Simon Niedermayr, Josef Stumpfegger, Rüdiger Westermann
  year: '2024'
  abstract: Recently, high-fidelity scene reconstruction with an optimized 3D Gaussian
    splat representation has been introduced for novel view synthesis from sparse
    image sets. Making such representations suitable for applications like network
    streaming and rendering on low-power devices requires significantly reduced memory
    consumption as well as improved rendering efficiency. We propose a compressed
    3D Gaussian splat representation that utilizes sensitivity-aware vector clustering
    with quantization-aware training to compress directional colors and Gaussian parameters.
    The learned codebooks have low bitrates and achieve a compression rate of up to
    31× on real-world scenes with only minimal degradation of visual quality. We demonstrate
    that the compressed splat representation can be efficiently rendered with hardware
    rasterization on lightweight GPUs at up to 4× higher framerates than reported
    via an optimized GPU compute pipeline. Extensive experiments across multiple datasets
    demonstrate the robustness and rendering speed of the proposed approach.
  project_page: https://keksboter.github.io/c3dgs/
  paper: https://arxiv.org/pdf/2401.02436.pdf
  code: https://github.com/KeKsBoTer/c3dgs
  video: null
  tags:
  - Code
  - Compression
  - Project
  thumbnail: assets/thumbnails/niedermayr2024compressed.jpg
  publication_date: '2023-11-17T14:40:43+00:00'
- id: zielonka2023drivable
  title: Drivable 3D Gaussian Avatars
  authors: Wojciech Zielonka, Timur Bagautdinov, Shunsuke Saito, Michael Zollhöfer,
    Justus Thies, Javier Romero
  year: '2023'
  abstract: 'We present Drivable 3D Gaussian Avatars (D3GA), the first 3D controllable
    model for human bodies rendered with Gaussian splats. Current photorealistic drivable
    avatars require either accurate 3D registrations during training, dense input
    images during testing, or both. The ones based on neural radiance fields also
    tend to be prohibitively slow for telepresence applications. This work uses the
    recently presented 3D Gaussian Splatting (3DGS) technique to render realistic
    humans at real-time framerates, using dense calibrated multi-view videos as input.
    To deform those primitives, we depart from the commonly used point deformation
    method of linear blend skinning (LBS) and use a classic volumetric deformation
    method: cage deformations. Given their smaller size, we drive these deformations
    with joint angles and keypoints, which are more suitable for communication applications.
    Our experiments on nine subjects with varied body shapes, clothes, and motions
    obtain higher-quality results than state-of-the-art methods when using the same
    training and test data.'
  project_page: https://zielon.github.io/d3ga/
  paper: https://arxiv.org/pdf/2311.08581.pdf
  code: null
  video: https://youtu.be/C4IT1gnkaF0?si=zUJLm8adM68pVvR8
  tags:
  - Avatar
  - Project
  - Video
  thumbnail: assets/thumbnails/zielonka2023drivable.jpg
  publication_date: '2023-11-14T22:54:29+00:00'
- id: yang2023realtime
  title: Real-time Photorealistic Dynamic Scene Representation and Rendering with
    4D Gaussian Splatting
  authors: Zeyu Yang, Hongye Yang, Zijie Pan, Xiatian Zhu, Li Zhang
  year: '2023'
  abstract: 'Reconstructing dynamic 3D scenes from 2D images and generating diverse
    views over time is challenging due to scene complexity and temporal dynamics.
    Despite advancements in neural implicit models, limitations persist: (i) Inadequate
    Scene Structure: Existing methods struggle to reveal the spatial and temporal
    structure of dynamic scenes from directly learning the complex 6D plenoptic function.
    (ii) Scaling Deformation Modeling: Explicitly modeling scene element deformation
    becomes impractical for complex dynamics. To address these issues, we consider
    the spacetime as an entirety and propose to approximate the underlying spatio-temporal
    4D volume of a dynamic scene by optimizing a collection of 4D primitives, with
    explicit geometry and appearance modeling. Learning to optimize the 4D primitives
    enables us to synthesize novel views at any desired time with our tailored rendering
    routine. Our model is conceptually simple, consisting of a 4D Gaussian parameterized
    by anisotropic ellipses that can rotate arbitrarily in space and time, as well
    as view-dependent and time-evolved appearance represented by the coefficient of
    4D spherindrical harmonics. This approach offers simplicity, flexibility for variable-length
    video and end-to-end training, and efficient real-time rendering, making it suitable
    for capturing complex dynamic scene motions. Experiments across various benchmarks,
    including monocular and multi-view scenarios, demonstrate our 4DGS model''s superior
    visual quality and efficiency.'
  project_page: https://fudan-zvg.github.io/4d-gaussian-splatting/
  paper: https://arxiv.org/pdf/2310.10642.pdf
  code: https://github.com/fudan-zvg/4d-gaussian-splatting
  video: https://youtu.be/3cXC9e4CujM
  tags:
  - Code
  - Dynamic
  - Project
  - Video
  thumbnail: assets/thumbnails/yang2023realtime.jpg
  publication_date: '2023-10-16T17:57:43+00:00'
- id: yi12023gaussiandreamer
  title: 'GaussianDreamer: Fast Generation from Text to 3D Gaussian Splatting with
    Point Cloud Priors'
  authors: Taoran Yi1, Jiemin Fang, Guanjun Wu1, Lingxi Xie, Xiaopeng Zhang,
  year: '2023'
  abstract: In recent times, the generation of 3D assets from text prompts has shown
    impressive results. Both 2D and 3D diffusion models can generate decent 3D objects
    based on prompts. 3D diffusion models have good 3D consistency, but their quality
    and generalization are limited as trainable 3D data is expensive and hard to obtain.
    2D diffusion models enjoy strong abilities of generalization and fine generation,
    but the 3D consistency is hard to guarantee. This paper attempts to bridge the
    power from the two types of diffusion models via the recent explicit and efficient
    3D Gaussian splatting representation. A fast 3D generation framework, named as
    GaussianDreamer, is proposed, where the 3D diffusion model provides point cloud
    priors for initialization and the 2D diffusion model enriches the geometry and
    appearance. Operations of noisy point growing and color perturbation are introduced
    to enhance the initialized Gaussians. Our GaussianDreamer can generate a high-quality
    3D instance within 25 minutes on one GPU, much faster than previous methods, while
    the generated instances can be directly rendered in real time.
  project_page: https://taoranyi.com/gaussiandreamer/
  paper: https://arxiv.org/pdf/2310.08529.pdf
  code: https://github.com/hustvl/GaussianDreamer
  video: null
  tags:
  - Code
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/yi12023gaussiandreamer.jpg
  publication_date: '2023-10-12T17:22:24+00:00'
- id: wu20234d
  title: 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering
  authors: Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei,
    Wenyu Liu, Tian Qi, Xinggang Wang
  year: '2023'
  abstract: Representing and rendering dynamic scenes has been an important but challenging
    task. Especially, to accurately model complex motions, high efficiency is usually
    hard to maintain. We introduce the 4D Gaussian Splatting (4D-GS) to achieve real-time
    dynamic scene rendering while also enjoying high training and storage efficiency.
    An efficient deformation field is constructed to model both Gaussian motions and
    shape deformations. Different adjacent Gaussians are connected via a HexPlane
    to produce more accurate position and shape deformations. Our 4D-GS method achieves
    real-time rendering under high resolutions, 70 FPS at a 800×800 resolution on
    an RTX 3090 GPU, while maintaining comparable or higher quality than previous
    state-of-the-art method.
  project_page: https://guanjunwu.github.io/4dgs/
  paper: https://arxiv.org/pdf/2310.08528.pdf
  code: https://github.com/hustvl/4DGaussians
  video: https://youtu.be/_wRbq8KnaVg
  tags:
  - Code
  - Dynamic
  - Project
  - Video
  thumbnail: assets/thumbnails/wu20234d.jpg
  publication_date: '2023-10-12T17:21:41+00:00'
- id: lin2023gaussianflow
  title: 'Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle'
  authors: Youtian Lin, Zuozhuo Dai, Siyu Zhu, Yao Yao
  year: '2023'
  abstract: We introduce Gaussian-Flow, a novel point-based approach for fast dynamic
    scene reconstruction and real-time rendering from both multi-view and monocular
    videos. In contrast to the prevalent NeRF-based approaches hampered by slow training
    and rendering speeds, our approach harnesses recent advancements in point-based
    3D Gaussian Splatting (3DGS). Specifically, a novel Dual-Domain Deformation Model
    (DDDM) is proposed to explicitly model attribute deformations of each Gaussian
    point, where the time-dependent residual of each attribute is captured by a polynomial
    fitting in the time domain, and a Fourier series fitting in the frequency domain.
    The proposed DDDM is capable of modeling complex scene deformations across long
    video footage, eliminating the need for training separate 3DGS for each frame
    or introducing an additional implicit neural field to model 3D dynamics. Moreover,
    the explicit deformation modeling for discretized Gaussian points ensures ultra-fast
    training and rendering of a 4D scene, which is comparable to the original 3DGS
    designed for static 3D reconstruction. Our proposed approach showcases a substantial
    efficiency improvement, achieving a 5× faster training speed compared to the per-frame
    3DGS modeling. In addition, quantitative results demonstrate that the proposed
    Gaussian-Flow significantly outperforms previous leading methods in novel view
    rendering quality.
  project_page: https://nju-3dv.github.io/projects/Gaussian-Flow
  paper: https://arxiv.org/pdf/2310.08528.pdf
  code: https://github.com/NJU-3DV/Gaussian-Flow
  video: null
  tags:
  - Code
  - Dynamic
  - Project
  thumbnail: assets/thumbnails/lin2023gaussianflow.jpg
  publication_date: '2023-10-12T17:21:41+00:00'
- id: tang2023dreamgaussian
  title: 'DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation'
  authors: Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, Gang Zeng
  year: '2023'
  abstract: Recent advances in 3D content creation mostly leverage optimization-based
    3D generation via score distillation sampling (SDS). Though promising results
    have been exhibited, these methods often suffer from slow per-sample optimization,
    limiting their practical usage. In this paper, we propose DreamGaussian, a novel
    3D content generation framework that achieves both efficiency and quality simultaneously.
    Our key insight is to design a generative 3D Gaussian Splatting model with companioned
    mesh extraction and texture refinement in UV space. In contrast to the occupancy
    pruning used in Neural Radiance Fields, we demonstrate that the progressive densification
    of 3D Gaussians converges significantly faster for 3D generative tasks. To further
    enhance the texture quality and facilitate downstream applications, we introduce
    an efficient algorithm to convert 3D Gaussians into textured meshes and apply
    a fine-tuning stage to refine the details. Extensive experiments demonstrate the
    superior efficiency and competitive generation quality of our proposed approach.
    Notably, DreamGaussian produces high-quality textured meshes in just 2 minutes
    from a single-view image, achieving approximately 10 times acceleration compared
    to existing methods.
  project_page: https://dreamgaussian.github.io/
  paper: https://arxiv.org/pdf/2309.16653.pdf
  code: https://github.com/dreamgaussian/dreamgaussian
  video: https://www.youtube.com/live/l956ye13F8M?si=ZkvFL_lsY5OQUB7e
  tags:
  - Code
  - Diffusion
  - Project
  - Video
  thumbnail: assets/thumbnails/tang2023dreamgaussian.jpg
  publication_date: '2023-09-28T17:55:05+00:00'
- id: chen2023textto3d
  title: Text-to-3D using Gaussian Splatting
  authors: Zilong Chen, Feng Wang, Huaping Liu
  year: '2023'
  abstract: In this paper, we present Gaussian Splatting based text-to-3D generation
    (GSGEN), a novel approach for generating high-quality 3D objects. Previous methods
    suffer from inaccurate geometry and limited fidelity due to the absence of 3D
    prior and proper representation. We leverage 3D Gaussian Splatting, a recent state-of-the-art
    representation, to address existing shortcomings by exploiting the explicit nature
    that enables the incorporation of 3D prior. Specifically, our method adopts a
    pro- gressive optimization strategy, which includes a geometry optimization stage
    and an appearance refinement stage. In geometry optimization, a coarse representation
    is established under a 3D geometry prior along with the ordinary 2D SDS loss,
    ensuring a sensible and 3D-consistent rough shape. Subsequently, the obtained
    Gaussians undergo an iterative refinement to enrich details. In this stage, we
    increase the number of Gaussians by compactness-based densification to enhance
    continuity and improve fidelity. With these designs, our approach can generate
    3D content with delicate details and more accurate geometry. Extensive evaluations
    demonstrate the effectiveness of our method, especially for capturing high-frequency
    components.
  project_page: https://gsgen3d.github.io/
  paper: https://arxiv.org/pdf/2309.16585.pdf
  code: https://github.com/gsgen3d/gsgen
  video: https://streamable.com/28snte
  tags:
  - Code
  - Diffusion
  - Project
  - Video
  thumbnail: assets/thumbnails/chen2023textto3d.jpg
  publication_date: '2023-09-28T16:44:31+00:00'
- id: yang2023deformable
  title: Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction
  authors: Ziyi Yang, Xinyu Gao, Wen Zhou, Shaohui Jiao, Yuqing Zhang, Xiaogang Jin
  year: '2023'
  abstract: Implicit neural representation has opened up new avenues for dynamic scene
    reconstruction and rendering. Nonetheless, state-of-the-art methods of dynamic
    neural rendering rely heavily on these implicit representations, which frequently
    struggle with accurately capturing the intricate details of objects in the scene.
    Furthermore, implicit methods struggle to achieve real-time rendering in general
    dynamic scenes, limiting their use in a wide range of tasks. To address the issues,
    we propose a deformable 3D Gaussians Splatting method that reconstructs scenes
    using explicit 3D Gaussians and learns Gaussians in canonical space with a deformation
    field to model monocular dynamic scenes. We also introduced a smoothing training
    mechanism with no extra overhead to mitigate the impact of inaccurate poses in
    real datasets on the smoothness of time interpolation tasks. Through differential
    gaussian rasterization, the deformable 3D Gaussians not only achieve higher rendering
    quality but also real-time rendering speed. Experiments show that our method outperforms
    existing methods significantly in terms of both rendering quality and speed, making
    it well-suited for tasks such as novel-view synthesis, time synthesis, and real-time
    rendering.
  project_page: https://ingra14m.github.io/Deformable-Gaussians/
  paper: https://arxiv.org/pdf/2309.13101.pdf
  code: https://github.com/ingra14m/Deformable-3D-Gaussians
  video: null
  tags:
  - Code
  - Project
  thumbnail: assets/thumbnails/yang2023deformable.jpg
  publication_date: '2023-09-22T16:04:02+00:00'
- id: luiten2023dynamic
  title: 'Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis'
  authors: Jonathon Luiten, Georgios Kopanas, Bastian Leibe, Deva Ramanan
  year: '2023'
  abstract: We present a method that simultaneously addresses the tasks of dynamic
    scene novel-view synthesis and six degree-of-freedom (6-DOF) tracking of all dense
    scene elements. We follow an analysis-by-synthesis framework, inspired by recent
    work that models scenes as a collection of 3D Gaussians which are optimized to
    reconstruct input images via differentiable rendering. To model dynamic scenes,
    we allow Gaussians to move and rotate over time while enforcing that they have
    persistent color, opacity, and size. By regularizing Gaussians’ motion and rotation
    with local rigidity constraints, we show that our Dynamic 3D Gaussians correctly
    model the same area of physical space over time, including the rotation of that
    space. Dense 6-DOF tracking and dynamic reconstruction emerges naturally from
    persistent dynamic view synthesis, without requiring any correspondence or flow
    as input. We demonstrate a large number of downstream applications enabled by
    our representation, including first-person view synthesis, dynamic compositional
    scene synthesis, and 4D video editing.
  project_page: https://dynamic3dgaussians.github.io/
  paper: https://arxiv.org/pdf/2308.09713.pdf
  code: https://github.com/JonathonLuiten/Dynamic3DGaussians
  video: https://www.youtube.com/live/hDuy1TgD8I4?si=6oGN0IYnPRxOibpg
  tags:
  - Code
  - Dynamic
  - Project
  - Video
  thumbnail: assets/thumbnails/luiten2023dynamic.jpg
  publication_date: '2023-08-18T17:59:21+00:00'
  date_source: arxiv
- id: kerblgaussian
  title: Gaussian Splatting for Real-Time Radiance Field Rendering
  authors: Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George Drettakis
  year: '2023'
  abstract: Radiance Field methods have recently revolutionized novel-view synthesis
    of scenes captured with multiple photos or videos. However, achieving high visual
    quality still requires neural networks that are costly to train and render, while
    recent faster methods inevitably trade off speed for quality. For unbounded and
    complete scenes (rather than isolated objects) and 1080p resolution rendering,
    no current method can achieve real-time display rates. We introduce three key
    elements that allow us to achieve state-of-the-art visual quality while maintaining
    competitive training times and importantly allow high-quality real-time (≥ 30
    fps) novel-view synthesis at 1080p resolution. First, starting from sparse points
    produced during camera calibration, we represent the scene with 3D Gaussians that
    preserve desirable properties of continuous volumetric radiance fields for scene
    optimization while avoiding unnecessary computation in empty space; Second, we
    perform interleaved optimization/density control of the 3D Gaussians, notably
    optimizing anisotropic covariance to achieve an accurate representation of the
    scene; Third, we develop a fast visibility-aware rendering algorithm that supports
    anisotropic splatting and both accelerates training and allows real-time rendering.
    We demonstrate state-of-the-art visual quality and real-time rendering on several
    established datasets.
  project_page: https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/
  paper: https://arxiv.org/pdf/2308.04079.pdf
  code: https://github.com/graphdeco-inria/gaussian-splatting
  video: https://youtu.be/T_kXY43VZnk?si=DrkbDFxQAv5scQNT
  tags:
  - Classic Work
  - Code
  - Dynamic
  - Project
  - Rendering
  - Video
  thumbnail: assets/thumbnails/kerblgaussian.jpg
  publication_date: '2023-08-08T06:37:06+00:00'
  date_source: arxiv
- id: shao2023control4d
  title: 'Control4D: Efficient 4D Portrait Editing with Text'
  authors: Ruizhi Shao, Jingxiang Sun, Cheng Peng, Zerong Zheng, Boyao Zhou, Hongwen
    Zhang, Yebin Liu
  year: '2023'
  abstract: We introduce Control4D, an innovative framework for editing dynamic 4D
    portraits using text instructions. Our method addresses the prevalent challenges
    in 4D editing, notably the inefficiencies of existing 4D representations and the
    inconsistent editing effect caused by diffusion-based editors. We first propose
    GaussianPlanes, a novel 4D representation that makes Gaussian Splatting more structured
    by applying plane-based decomposition in 3D space and time. This enhances both
    efficiency and robustness in 4D editing. Furthermore, we propose to leverage a
    4D generator to learn a more continuous generation space from inconsistent edited
    images produced by the diffusion-based editor, which effectively improves the
    consistency and quality of 4D editing. Comprehensive evaluation demonstrates the
    superiority of Control4D, including significantly reduced training time, high-quality
    rendering, and spatial-temporal consistency in 4D portrait editing.
  project_page: https://control4darxiv.github.io/
  paper: https://arxiv.org/pdf/2305.20082.pdf
  code: null
  video: null
  tags:
  - Dynamic
  - Editing
  - Project
  thumbnail: assets/thumbnails/shao2023control4d.jpg
  publication_date: '2023-05-31T17:55:28+00:00'
- id: blattmann2023align
  title: 'Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed
    Diffusion Models'
  authors: Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim,
    Sanja Fidler, Karsten Kreis
  year: '2023'
  abstract: Recent advancements in 3D reconstruction from single images have been
    driven by the evolution of generative models. Prominent among these are methods
    based on Score Distillation Sampling (SDS) and the adaptation of diffusion models
    in the 3D domain. Despite their progress, these techniques often face limitations
    due to slow optimization or rendering processes, leading to extensive training
    and optimization times. In this paper, we introduce a novel approach for single-view
    reconstruction that efficiently generates a 3D model from a single image via feed-forward
    inference. Our method utilizes two transformer-based networks, namely a point
    decoder and a triplane decoder, to reconstruct 3D objects using a hybrid Triplane-Gaussian
    intermediate representation. This hybrid representation strikes a balance, achieving
    a faster rendering speed compared to implicit representations while simultaneously
    delivering superior rendering quality than explicit representations. The point
    decoder is designed for generating point clouds from single images, offering an
    explicit representation which is then utilized by the triplane decoder to query
    Gaussian features for each point. This design choice addresses the challenges
    associated with directly regressing explicit 3D Gaussian attributes characterized
    by their non-structural nature. Subsequently, the 3D Gaussians are decoded by
    an MLP to enable rapid rendering through splatting. Both decoders are built upon
    a scalable, transformer-based architecture and have been efficiently trained on
    large-scale 3D datasets. The evaluations conducted on both synthetic datasets
    and real-world images demonstrate that our method not only achieves higher quality
    but also ensures a faster runtime in comparison to previous state-of-the-art techniques.
  project_page: https://research.nvidia.com/labs/toronto-ai/AlignYourGaussians/
  paper: https://arxiv.org/pdf/2304.08818.pdf
  code: null
  video: null
  tags:
  - Diffusion
  - Project
  thumbnail: assets/thumbnails/blattmann2023align.jpg
  publication_date: '2023-04-18T08:30:32+00:00'