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DeepInverseRendering

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Source code for our paper Deep Inverse Rendering for High-resolution SVBRDF Estimation from an Arbitrary Number of Images

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# 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)*

![image](https://raw.githubusercontent.com/msraig/DeepInverseRendering/master/mvsvbrdf.png)

**More information (including our paper, supplementary, video and slides) can be found at [My Personal Page](https://gao-duan.github.io/).**

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](https://drive.google.com/open?id=17WrAZIkyejwRm5aKBtTvNHYe_3c6S9aE).
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)

```bash
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)

```bash
   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}
}
```

DeepInverseRendering

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