Official implementation of Residual NeRF.

Residual-NeRF: Learning Residual NeRFs for Transparent Object Manipulation
Bardienus P. Duisterhof¹, Yuemin Mao¹, Si Heng Teng¹, Jeffrey Ichnowski^1
1Carnegie Mellon University, the Robotics Institute
in International Conference on Robotics and Automation (ICRA) 2024

docker pull bartduis/torch-ngp:latest
docker run -it --runtime=nvidia --gpus all -v /path/to/Residual-NeRF:/workspace bartduis/torch-ngp:latest

pip install -r requirements.txt

conda env create -f environment.yml
conda activate torch-ngp

You can access the data from here. You can also download all data with the following command:

gdown --folder https://drive.google.com/drive/folders/15r_mLt8MD5-AuYfoWCLy72hD27-tHA2N --remaining-ok 

The data should have the following structure:

data
├── bowl_rgb
│   ├── train 
│   │   ├── r_0_-1.png
│   │   ├── r_0_0.png
│   │   └── ...
│   ├── test
│   │   ├── r_0_-1.png
│   │   ├── r_0_0.png
│   │   └── ...
│   ├── val
│   │   ├── r_0_-1.png
│   │   ├── r_0_0.png
│   │   └── ...
│   └── gt_depth.npy

For each image filename, the first integer is the view id whereas the second integer indicates a background image (-1) or an image with the object present (0).

The run_scripts folder contains the generic_script_eval.sh and generic_script_train.sh scripts. To train a vanilla, background, and residual NeRF on a dataset, run:

./run_scripts/generic_script_train.sh <GPU_ID> <ITERATIONS> <DATA_PATH> <WORKSPACE> <DOWN_SCALE> <OUT_FOLDER> <N_VIEWS>

Here:

• <GPU_ID> Is the GPU to use for training.
• Number iterations to train for.
• <DATA_PATH> Path to data folder in residual-NeRF format
• Name of workspace output that'll be created.
• Downscale factor which can be used to train on smaller images, with the provided 800 x 800 dataset 1.0 makes most sense.
• <OUT_FOLDER> output directory.
• <N_VIEWS> Number of views used for training of the residual and vanilla nerf. This can be used to create an unbalanced dataset with fewer images of the full scene.

Here is an example of what training on the provided data could look like:

./run_scripts/generic_script_train.sh 0 10000 data/bowl bowl 1.0 output 100

We will provide better documentation of the evaluation script, and the scripts in the scripts folder in the coming weeks after initial release.

This codebase built upon Torch-NGP, an excellent implementation of Instant-NGP in Pytorch with limited compromise.

@inproceedings{duisterhof2024residualnerf,
  title={Residual-NeRF: Learning Residual NeRFs for Transparent Object Manipulation}, 
  author={Bardienus P. Duisterhof and Yuemin Mao and Si Heng Teng and Jeffrey Ichnowski},
  year={2024},
  booktitle={ICRA 2024}
}