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MultiResolutionKernelPredictionCNN

A Multi-Resolution variant of Kernel Prediction CNN (MR-KP) denoiser

We have adapted the Multi-Resolution Kernel Prediction CNN (MR-KP) denoiser, which decreases the run time of a basic kernel prediction architecture to the order of tens of milliseconds (35ms on a Nvidia RTX 2080 GPU).

The structure of the network

The structure of the pyramid-denoiser:

Citation

If you find this implementation useful in your research, please consider citing:

@article{10.1145/3072959.3073708, 
  author = {Bako, Steve and Vogels, Thijs and Mcwilliams, Brian and Meyer, Mark and Nov\'{a}K, Jan and Harvill, Alex and Sen, Pradeep and Derose, Tony and Rousselle, Fabrice}, 
  title = {Kernel-Predicting Convolutional Networks for Denoising Monte Carlo Renderings}, 
  year = {2017}, 
  issue_date = {July 2017}, 
  publisher = {Association for Computing Machinery}, 
  address = {New York, NY, USA}, 
  volume = {36}, 
  number = {4}, 
  issn = {0730-0301}, 
  url = {https://doi.org/10.1145/3072959.3073708}, 
  doi = {10.1145/3072959.3073708}, 
  journal = {ACM Trans. Graph.}, 
  month = jul, 
  articleno = {97}, 
  numpages = {14}, 
  keywords = {global illumination, Monte Carlo denoising, Monte Carlo rendering} 
}
@inproceedings {Meng2020Real, 
  booktitle = {Eurographics Symposium on Rendering 2020}, 
  title = {{Real-time Monte Carlo Denoising with the Neural Bilateral Grid}}, 
  author = {Xiaoxu Meng, Quan Zheng, Amitabh Varshney, Gurprit Singh, Matthias Zwicker}, 
  year = {2020}, 
  publisher = {The Eurographics Association}, 
}

Prerequisite Installation

• Python3
• TensorFlow 1.13.1
• Pillow 6.1.0 (or newer)
• scikit-image 0.16.1 (or newer)
• OpenEXR 1.3.2 (or newer)

Test with the Pre-trained Models

1. Clone this repo, and we'll call the directory ${MCDNBG_ROOT}.
2. Download pre-trained models "classroom" and put the pretrained model to ${MCDNBG_ROOT}/classroom/model.
3. Download the 1-spp dataset (19GB) or the packed testdata for scene "classroom" (1.4GB). If you are using
4. Recompile the bilateral kernels by running

cd 0_kernel_functions
./kernel_filter.sh
cd ..
cd 0_upsampling
./upsampling.sh
cd ..

5. Apply the denoiser by running

python network_test.py

• Input
  • If you use the 1-spp dataset (19GB), please change the data-path in the argument list:

  python network_test.py -d ${your-data-path}
  

  • if you use the packed testdata for scene "classroom" (1.4GB), please put the tfrecords file in ${MCDNBG_ROOT}.
• There are a few options in the arguments:

  --export_exr ## export the exr file of the 1-spp radiance, denoised image, and ground truth
  --export_all ## export all the denoised images from the lower-resolution layers
  

6. Evaluate the outputs by running:

python evaluation.py -d "classroom"

• The per-frame PSNR, SSIM, RMSE, SMAPE, and relative-MSE are saved in ${MCDNBG_ROOT}/classroom/result/evaluations
• The SSIM errormaps and relative-MSE errormaps are saved in ${MCDNBG_ROOT}/classroom/result/evaluations

Retrain Your Own Model

Run "python network_train.py"

Comparison with Neural Bilateral Grid Denoiser (MR-KP)

Please visit our implementation of MR-KP for more information.