Awesome

This is a reimplementation of

Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer (NeurIPS 2019)

Wenzheng Chen, Jun Gao*, Huan Ling*, Edward J. Smith*, Jaakko Lehtinen, Alec Jacobson, Sanja Fidler

[Paper] [Project Page]

It contains training and inference scripts for Single Image 3D Reconstruction task, using Kaolin library DIB-R renderer. It is suitable for reproduction of the publication:

If you'd like to see the original implementation of the renderer you can check the legacy code (Renderer only).

Usage

requirements

We recommneded to use conda to install all the dependencies.

Tested under python3.7.10, cuda 11.1, kaolin0.9.0

# pytorch
conda create --name dibr python=3.7.10
conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=11.0 -c pytorch
# other libararies
pip install opencv-python tensorboardX  
# import render from kaolin since it is optimized
git clone --recursive https://github.com/NVIDIAGameWorks/kaolin
cd kaolin
python setup.py develop
cd ..

dataset

To better reproduce the model, we use the public rendered images of shapenet from DVR repo https://github.com/autonomousvision/differentiable_volumetric_rendering. Note it is pretty big (31.5GB). Please downlaod the choice 2 to get the renderings for 2D supervised models.

train

git clone repo
cd repo
cd train
python train.py --datafolder YOUR_DATASET_FOLDER  --svfolder YOUR_SAVING_FOLDER

eval

python eval.py --datafolder YOUR_DATASET_FOLDER  --svfolder YOUR_SAVING_FOLDER --iterbe YOUR_MODE_ITER --viewnum 1
cd ../chamfer
python check_chamfer.py  --folder YOUR_SAVING_FOLDER --gt_folder YOUR_DATASET_FOLDER 

performance

tested with checkpoint 469999 iterations.

Citations

Please consider the following citation if you find our code is useful:

@inproceedings{chen2019_dibr,
 author = {Chen, Wenzheng and Ling, Huan and Gao, Jun and Smith, Edward and Lehtinen, Jaakko and Jacobson, Alec and Fidler, Sanja},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer},
 url = {https://proceedings.neurips.cc/paper/2019/file/f5ac21cd0ef1b88e9848571aeb53551a-Paper.pdf},
 volume = {32},
 year = {2019}
}