Updated READMEs

.gitignore

@@ -1,4 +1,4 @@
-/weights
+*.pth
 *.pyc
 *._
 __pycache__

evaluate/readme.md

@@ -66,7 +66,8 @@ Only `--bop` is needed to be passed to load a bop scene. You can pass which scen
 
 We assume that you have the intrinsics stored in the camera data. If you do not have them, the script uses 512 x 512 with a fov of 0.78. If the camera data is complete, like with NViSII data, it will use the camera intrinsics. 
 
+<!-- 
 # TODO 
 - Make a `requirement.txt` file. 
 - Possibly subsamble vertices so computation is faster
-<!-- - make a script to visualize the json files from DOPE -->
+- make a script to visualize the json files from DOPE -->

inference/README.md

@@ -22,7 +22,7 @@ The `inference.py` script will take a trained model to run inference. In order t
 Below is an example of running inference:
 
 ```
-python inference.py --weights ../output/weights --data ../sample_data --object cracker
+python inference.py --weights ../weights --data ../sample_data --object cracker
 ```
 
 ### Configuration Files

readme.md

@@ -1,79 +1,48 @@
 [![License CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-blue.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode)
 ![Python 3.8](https://img.shields.io/badge/python-3.8-blue.svg)
-# Deep Object Pose Estimation - ROS Inference
+# Deep Object Pose Estimation
 
-This is the official DOPE ROS package for detection and 6-DoF pose estimation of **known objects** from an RGB camera.  The network has been trained on the following YCB objects:  cracker box, sugar box, tomato soup can, mustard bottle, potted meat can, and gelatin box.  For more details, see our [CoRL 2018 paper](https://arxiv.org/abs/1809.10790) and [video](https://youtu.be/yVGViBqWtBI).
+This is the official DOPE ROS package for detection and 6-DoF pose estimation of **known objects** from an RGB camera.  For full details, see our [CoRL 2018 paper](https://arxiv.org/abs/1809.10790) and [video](https://youtu.be/yVGViBqWtBI).
 
 
 ![DOPE Objects](dope_objects.png)
 
-## Updates
 
-2024/03/07 - New training code. New synthetic data generation code, using Blenderproc. Repo reorganization
+## Contents
+This repository contains complete code for [training](train), [inference](inference), numerical [evaluation](evaluate) of results, and synthetic [data generation](data_generation) using either  [NVISII](https://github.com/owl-project/NVISII) or [Blenderproc](https://github.com/DLR-RM/BlenderProc).  We also provide a [ROS1 Noetic package](ros1) that performs inference on images from a USB camera.
 
-2022/07/13 - Added a script with a simple example for computing the ADD and ADD-S metric on data. Please refer to [script/metrics/](https://github.com/NVlabs/Deep_Object_Pose/tree/master/scripts/metrics). 
+Hardware-accelerated ROS2 inference can be done with the
+[Isaac ROS DOPE](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_pose_estimation/tree/main/isaac_ros_dope) project.
 
-2022/03/30 - Update on the NViSII script to handle [symmetrical objects](https://github.com/NVlabs/Deep_Object_Pose/tree/master/scripts/nvisii_data_gen#handling-objects-with-symmetries).  Also the NViSII script is compatible with the original training script. Thanks to Martin Günther. 
-
-2021/12/13 - Added a NViSII script to generate synthetic data for training DOPE. See this [readme](https://github.com/NVlabs/Deep_Object_Pose/tree/master/scripts/nvisii_data_gen) for more details. We also added the update training and inference (without ROS) scripts for the NViSII paper [here](https://github.com/NVlabs/Deep_Object_Pose/tree/master/scripts/train2). 
-
-2021/10/20 - Added ROS2 Foxy inference support through [Isaac ROS DOPE package](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_pose_estimation) for Jetson and x86+CUDA-capable GPU.
-
-2021/08/07 - Added publishing belief maps. Thank you to Martin Günther. 
-
-2020/03/09 - Added HOPE [weights to google drive](https://drive.google.com/open?id=1DfoA3m_Bm0fW8tOWXGVxi4ETlLEAgmcg), [the 3d models](https://drive.google.com/drive/folders/1jiJS9KgcYAkfb8KJPp5MRlB0P11BStft), and the objects dimensions to config. [Tremblay et al., IROS 2020](https://arxiv.org/abs/2008.11822).  The HOPE dataset can be found [here](https://github.com/swtyree/hope-dataset/) and is also part of the [BOP challenge](https://bop.felk.cvut.cz/datasets/#HOPE)
-
-
-<br>
-<br>
 
 ## Tested Configurations
 
-We have tested on Ubuntu 20.04 with ROS Noetic with an NVIDIA Titan X and RTX 2080ti with Python 3.8. The code may work on other systems.
+We have tested our standalone training, inference and evaluation scripts on Ubuntu 20.04 and 22.04 with Python 3.8+, using an NVIDIA Titan X, 2080Ti, and Titan RTX. 
 
----
-***NOTE***
-
-For hardware-accelerated ROS2 inference support, please visit [Isaac ROS DOPE](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_pose_estimation/tree/main/isaac_ros_dope) which has been tested with ROS2 Foxy on Jetson AGX Xavier/JetPack 4.6 and on x86/Ubuntu 20.04 with RTX3060i.
+The ROS1 node has been tested with ROS Noetic using Python 3.10. The Isaac ROS2 DOPE node has been tested with ROS2 Foxy on Jetson AGX Xavier with JetPack 4.6; and on x86/Ubuntu 20.04 with a NVIDIA Titan X, 2080Ti, and Titan RTX.  
 
----
-<br>
-<br>
 
-## Synthetic Data Generation
-Code and instructions for generating synthetic training data are found in the `data_generation` directory. There are two options for the render engine: you can use [NVISII](https://github.com/owl-project/NVISII) or [Blenderproc](https://github.com/DLR-RM/BlenderProc)
+## Datasets
 
-## Training
-Code and instructions for training DOPE are found in the `train` directory.
+We have trained and tested DOPE with two publicaly available datasets: YCB, and HOPE. The trained weights can be [downloaded from Google Drive](https://drive.google.com/drive/folders/1DfoA3m_Bm0fW8tOWXGVxi4ETlLEAgmcg).
 
-## Inference
-Code and instructions for command-line inference using PyTorch are found in the `inference` directory
 
-## Evaluation
-Code and instructions for evaluating the quality of your results are found in the `evaluate` directory
 
----
+### YCB 3D Models
+YCB models can be downloaded from the [YCB website](http://www.ycbbenchmarks.com/), or by  using [NVDU](https://github.com/NVIDIA/Dataset_Utilities) (see the `nvdu_ycb` command).  
 
-## YCB 3D Models
 
-DOPE returns the poses of the objects in the camera coordinate frame.  DOPE uses the aligned YCB models, which can be obtained using [NVDU](https://github.com/NVIDIA/Dataset_Utilities) (see the `nvdu_ycb` command).
+### HOPE 3D Models
+The [HOPE dataset](https://github.com/swtyree/hope-dataset/) is a collection of RGBD images and video sequences with labeled 6-DoF poses for 28 toy grocery objects.  The 3D models [can be  downloaded here](https://drive.google.com/drive/folders/1jiJS9KgcYAkfb8KJPp5MRlB0P11BStft). 
+The folders are organized in the style of the YCB 3d models. 
 
----
+The physical objects can be purchased online (details and links to Amazon can be found in the [HOPE repository README](https://github.com/swtyree/hope-dataset/).
 
-## HOPE 3D Models
+<br><br>
 
-![HOPE 3D models rendered in UE4](https://i.imgur.com/V6wX64p.png)
+---
 
-We introduce new toy 3d models that you download [here](https://drive.google.com/drive/folders/1jiJS9KgcYAkfb8KJPp5MRlB0P11BStft). 
-The folders are arranged like the YCB 3d models organization. 
-You can buy the real objects using the following links 
-[set 1](https://www.amazon.com/gp/product/B071ZMT9S2), 
-[set 2](https://www.amazon.com/gp/product/B007EA6PKS), 
-[set 3](https://www.amazon.com/gp/product/B00H4SKSPS), 
-and 
-[set 4](https://www.amazon.com/gp/product/B072M2PGX9). 
 
-The HOPE dataset can be found [here](https://github.com/swtyree/hope-dataset/) and is also part of the [BOP challenge](https://bop.felk.cvut.cz/datasets/#HOPE).
 
 ## How to cite DOPE 
 
@@ -90,7 +59,7 @@ If you use this tool in a research project, please cite as follows:
 
 ## License
 
-Copyright (C) 2018 NVIDIA Corporation. All rights reserved. Licensed under the [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+Copyright (C) 2018-2024 NVIDIA Corporation. All rights reserved. This code is licensed under the [NVIDIA Source Code License](https://github.com/NVlabs/HANDAL/blob/main/LICENSE.txt).
 
 
 ## Acknowledgment

train/README.md

@@ -1,6 +1,6 @@
 # Deep Object Pose Estimation (DOPE) - Training 
 
-This repo contains a simplified version of the **training** script for DOPE.
+This repo contains a simplified version of the training pipeline for DOPE.
 Scripts for inference, evaluation, and data visualization can be found in this repo's top-level directories `inference` and `evaluate`.
 
 A user report of training DOPE on a single GPU using NVISII-created synthetic data can [be found here](https://github.com/NVlabs/Deep_Object_Pose/issues/155#issuecomment-791148200).
@@ -18,7 +18,7 @@ source ./output/dope_training/bin/activate
 ---
 To install the required dependencies, run:
 ```
-pip install -r requirements.txt
+pip install -r ../requirements.txt
 ```
 
 ## Training
@@ -65,6 +65,3 @@ python debug.py --data PATH_TO_IMAGES
 2. If you are running into dependency issues when installing, 
 you can try to install the version specific dependencies that are commented out in `requirements.txt`. Be sure to do this in a virtual environment.
 
-## License
-
-Copyright (C) 2018 NVIDIA Corporation. All rights reserved. Licensed under the [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).

weights/readme.md

@@ -1 +1,4 @@
-This is where you need to store the weights. 
+We have trained and tested DOPE with two publicaly available datasets: YCB, and HOPE. These trained weights can be 
+[downloaded from Google Drive](https://drive.google.com/drive/folders/1DfoA3m_Bm0fW8tOWXGVxi4ETlLEAgmcg).
+
+