DeepInverseRendering

Source code of our paper:

Deep Inverse Rendering for High-resolution SVBRDF Estimation from an Arbitrary Number of Images

Duan Gao, Xiao Li, Yue Dong, Pieter Peers, Kun Xu, Xin Tong.

ACM Transactions on Graphics (Proceedings of SIGGRAPH 2019)*

More information (including our paper, supplementary, video and slides) can be found at My Personal Page.

If you have any questions about our paper, please feel free to contact me (gao-d17@mails.tsinghua.edu.cn)

Pretrained models

Our pretrained SVBRDF auto-encoder can be downloaded from here. Download the pretrained model and extract it into ./model/.

Dependencies

• Python (with opencv-python, numpy; test on Python 3.6)

• Tensorflow-gpu (test on tensorflow>1.10)

Usage

• Test mode (SVBRDF map as input)

python3 main.py 
   --N 20                                       # number of input images
   --checkpoint ../model/                       # pretrained model of auto-encoder
   --dataDir ../example_data/example_svbrdf     # folder contains a set of input images
   --logDir ../log_test_example                 # output folder
   --initDir  ../example_data/example_init      # folder contains initial SVBRDF maps or initial code 
   --network network_ae_fixBN                   # network architecture (default: network_ae_fixBN)
   
   --init_method svbrdf 
   --input_type svbrdf 
   --wlv_type load
   --wlvDir ../example_data/example_wlv

• Eval mode (captured images as input)

   python3 main.py                    
      --dataDir ../example_data/example_images    # LDR images are given in example_images    
      --input_type image 
      ... 

• SVBRDF format

  normal, diffuse, roughness, specular

  (diffuse map is in srgb color space (gamma 2.2), other maps are in linear color space)

• Command arguments:

-- input_type: ['image', 'svbrdf']
   
   'image':  a set of images, used in evaluate.
   
   'svbrdf':  a set of SVBRDFs, used in testing.
   
   
-- init_method: ['svbrdf', 'code', 'rand']

 'svbrdf': the estimated SVBRDF (using our encoder to embedding it into our latent space)
 
 'code':  the latent code (numpy array)
 
 'rand': random initialization the latent code


-- wlv_type: ['random', 'load']
   
   'random': random generate camera position and light position
   
   'load': load camera position and light position from file
   

Citation

If you use our code or pretrained models, please cite as following:

@article{gao2019deep,
  title={Deep inverse rendering for high-resolution SVBRDF estimation from an arbitrary number of images},
  author={GAO, DUAN and Li, Xiao and Dong, Yue and Peers, Pieter and Xu, Kun and Tong, Xin},
  journal={ACM Transactions on Graphics (TOG)},
  volume={38},
  number={4},
  pages={134},
  year={2019},
  month={July},
  publisher={ACM}
}