• Set up the python environment

conda create -n invrender python=3.7
conda activate invrender

pip install -r requirement.txt

• Download our example synthetic dataset from Google Drive

Taking the scene hotdog as an example, the training process is as follows.

1. Optimize geometry and outgoing radiance field from multi-view images. (Same as IDR)

   cd code
   python training/exp_runner.py --conf confs_sg/default.conf \
                                 --data_split_dir ../Synthetic4Relight/hotdog \
                                 --expname hotdog \
                                 --trainstage IDR \
                                 --gpu 1

2. Draw sample rays above surface points to train the indirect illumination and visibility MLP.

   python training/exp_runner.py --conf confs_sg/default.conf \
                                 --data_split_dir ../Synthetic4Relight/hotdog \
                                 --expname hotdog \
                                 --trainstage Illum \
                                 --gpu 1

3. Jointly optimize diffuse albedo, roughness and direct illumination.

   python training/exp_runner.py --conf confs_sg/default.conf \
                                 --data_split_dir ../Synthetic4Relight/hotdog \
                                 --expname hotdog \
                                 --trainstage Material \
                                 --gpu 1

• Generate videos under novel illumination.

  python scripts/relight.py --conf confs_sg/default.conf \
                            --data_split_dir ../Synthetic4Relight/hotdog \
                            --expname hotdog \
                            --timestamp latest \
                            --gpu 1

@inproceedings{zhang2022invrender,
  title={Modeling Indirect Illumination for Inverse Rendering},
  author={Zhang, Yuanqing and Sun, Jiaming and He, Xingyi and Fu, Huan and Jia, Rongfei and Zhou, Xiaowei},
  booktitle={CVPR},
  year={2022}
}

Acknowledgements: part of our code is inherited from IDR and PhySG. We are grateful to the authors for releasing their code.