Skip to content

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

License

Notifications You must be signed in to change notification settings

zju3dv/InvRender

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

5 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Modeling Indirect Illumination for Inverse Rendering

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.

About

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

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages