Source Code for SIGGRAPH Asia 2021 Paper "Ensemble Denoising for Monte Carlo Renderings"

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Artificial Intelligence EnsembleDenoising
Overview

Ensemble Denoising

Source Code for SIGGRAPH Asia 2021 Paper Ensemble Denoising for Monte Carlo Renderings.

Paper | Code | Talk Slides

Dependencies

  • A C++17 conformant compiler for building the code
  • CMake for configuring the project
  • Eigen3 for linear algebra data structures and operations
  • OpenCV for image IO and basic operators
  • cxxopts (included as a git submodule) for parsing CLI options
  • fbstab (optional, included as a git submodule) for linearly constrained quadratic programming as the baseline solver

Compiling the Project

First, please make sure that Eigen3 and OpenCV are properly install in your system and can be found by CMake.

To clone the repository:

git clone --recursive https://github.com/Mike-Leo-Smith/EnsembleDenoising.git

Then, use CMake to configure the build:

cmake -S <project-dir> -B <build-dir> -D CMAKE_BUILD_TYPE=Release

Note: to help CMake find Eigen3 and OpenCV, you may need to provide -D Eigen3_DIR=... and -D OpenCV_DIR=... as well. Also, you can use the -G option to specify your favorite generator, and -D CMAKE_CXX_COMPILER=<...> to choose the C++ compiler.

After the project is properly configured, use the following command to build the program:

cmake --build <build-dir>

And the executable EnsembleDenosing should appear in if successfully built.

Preparing Input Data

The input data should be organized in the following structure:


   
    
  │
  ├─ color.exr, colorA.exr, colorB.exr, albedo.exr, normal.exr
  │
  ├─ 
    
     
  │    │
  │    └─ 
     
      .exr 
      
       A.exr 
       
        B.exr │ ├─ 
        
          │ │ │ └─ 
         
          .exr 
          
           A.exr 
           
            B.exr ...

For example, if denoising-data is the input directory and two denoisers oidn and nfor are used, the directory structure should be

denoising-data
  │
  ├─ color.exr, colorA.exr, colorB.exr, albedo.exr, normal.exr
  │
  ├─ oidn
  │    │
  │    └─ oidn.exr oidnA.exr oidnB.exr
  │
  └─ nfor
       │
       └─ nfor.exr nforA.exr nforB.exr

You can use any scene, renderer, and denoiser to generate these data. In the paper, we use Tungsten for rendering.

Running the Program

To print the help message:

./EnsembleDenoising -h

To perform the ensemble denoising:

./EnsembleDenoising -i 
   
     -o 
    
      -m 
     
      [,
      
       [...]]
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Comments
  • How to generate nforA.exr and nforB.exr？

    How to generate nforA.exr and nforB.exr？

    In my view, the nfor.exr can be generated from color, colorA, colorB and many other features.

    But hot to generate nforA.exr or nforB.exr？

    Is nforA.exr generated from colorA, colorAA, colorAB and other features？

    opened by LZleejean 8
  • Could you provide the implementation of other algorithms?

    Could you provide the implementation of other algorithms?

    This paper is such great work and evaluate so many existing algorithms.

    But I found that some algorithms are not easy to reimplement. Therefore, could you provide your implementation, like RDFC?

    opened by LZleejean 4
  • Using weight channel 1 instead of 3.

    Using weight channel 1 instead of 3.

    I would like to also see the results of using weight channel of 1 applied to the pixell instead of weight channel of 3 applied to RGB channel each. I think a slight modification would do, but I cannot find which to change. Could you provide specific details on which part should I change to do so? Thank you in advance!

    opened by qbhan 1
Owner
郑少锟
水水的水锟
郑少锟
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