Point Cloud Occupancy with Dynamic Planes

This Repository contains code for reconstructing 3D point clouds using the Occupancy Predictions of a small and sparse subsets of points. This work is based on Lionar, Stefan, et al. "Dynamic Plane Convolutional Occupancy Networks" Proceedings of the IEEE/CVF Winter Conference of Applications of Computer Vision, 2021 with FAUST Dataset.

Table of Contents

• Repository-Structure
• Installation
• Dataset
• Architecture

Repository Structure

PointCloud3D/
├── README.md
├── requirements.txt
├── PointCloud3D.ipynb
├── .gitignore
├── Media/
│    └── images
│
├── Src/
│    ├── dataset.py
│    ├── encoder.py
│    ├── plane_predictor.py
│    ├── unet.py
│    └── utils.py
│    
├── Papers/
│    ├── dynamic_plane_conv.pdf
│    └── occupancy_net_mise.pdf
│
└── Slides/
     ├── main.pdf
     └── main.tex

Installation

1. Clone the repository:

   git clone https://github.com/EugenioBugli/3DPointCloud.git

2. Install dependencies:

   pip install -r <Folder>/3DPointCloud/requirements.txt

3. You can run the Code directly from the Notebook

Dataset

FAUST Dataset

Architecture

The Architecture used has an Encoder-Decoder structure and takes a Noisy Cloud as input for the Encoder and a Sampled Cloud for the Decoder.

• Noisy Cloud: it's composed by 3000 points sampled over the surface of the starting mesh with the addition of Gaussian noise.

• Sampled Cloud: it's composed by 2048 points sampled over the bounding box containing the starting mesh.

During Training we use Binary Cross Entropy (BCE) between the occupancy prediction and the ground truth occupancy, while during Inference we Multiresolution IsoSurface Extraction (MISE) to reconstruct the meshes.