Code for "Modeling Indirect Illumination for Inverse Rendering", CVPR 2022

Overview

Modeling Indirect Illumination for Inverse Rendering

Project Page | Paper | Data

Preparation

  • Set up the python environment
conda create -n invrender python=3.7
conda activate invrender

pip install -r requirement.txt

Run the code

Training

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

Relighting

  • 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

Citation

@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.

Owner
ZJU3DV
ZJU3DV is a research group of State Key Lab of CAD&CG, Zhejiang University. We focus on the research of 3D computer vision, SLAM and AR.
ZJU3DV
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