Input-Dependent Uncorrelated Weighting for Monte Carlo Denoising

Jonghee Back¹, Binh-Son Hua², Toshiya Hachisuka³, Bochang Moon¹

GIST¹, Trinity College Dublin², University of Waterloo³

Overview

This code is the official implementation of SIGGRAPH Asia 2023 paper, Input-Dependent Uncorrelated Weighting for Monte Carlo Denoising. For more detailed information, please refer to our project page or other materials as below:

• Project page
• Paper
• Supplementary report
• Interactive viewer

Usage

Running code

First, CMake, CUDA, OpenEXR and zlib are required to run the code. Please clone repositories for OpenEXR and zlib into src/ext folder and rename two folders to openexr and zlib, respectively. After all dependencies are set, please use CMake as follows (or running CMake GUI):

mkdir build
cd build
cmake ..

After building the generated project, you can run the denoiser with some provided example data (refer to below) in the following manner:

./DenoiserTester test_scenes/input-crn test_scenes/reference bathroom crn 128

Please check the print message for more detailed usage:

./DenoiserTester -h

We have tested the code on machines with the following environments:

• Windows 10, Visual Studio 2019, CUDA 11.3
• Ubuntu 20.04, Clang 10.0.0, CUDA 11.6

Example data

The example data consists of pairs of unbiased independent and correlated pixel estimates (common random number (CRN) and L2 reconstruction for gradient-domain rendering), rendered by gradient-domain rendering framework on top of Mitsuba renderer. The example data can be available here:

• Example data (primal-domain)
• Example data (gradient-domain)

License

All source codes are released under a BSD License.

Citation

@inproceedings{Back23,
author = {Back, Jonghee and Hua, Binh-Son and Hachisuka, Toshiya and Moon, Bochang},
title = {Input-Dependent Uncorrelated Weighting for Monte Carlo Denoising},
year = {2023},
isbn = {9798400703157},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3610548.3618177},
doi = {10.1145/3610548.3618177},
booktitle = {SIGGRAPH Asia 2023 Conference Papers},
articleno = {9},
numpages = {10},
keywords = {Monte Carlo denoising, unbiased denoising, input-dependent weighting, uncorrelated weighting},
location = {<conf-loc>, <city>Sydney</city>, <state>NSW</state>, <country>Australia</country>, </conf-loc>},
series = {SA '23}
}

Contact

If there are any questions, issues or comments, please feel free to send an e-mail to jongheeback@gm.gist.ac.kr.