Neural Denoising for Spectral Monte Carlo Rendering
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Spectral Monte Carlo (MC) rendering is still to be largely adopted partially due to the specific noise, called color noise, induced by wavelength-dependent phenomenons. Motivated by the recent advances in Monte Carlo noise reduction using Deep Learning, we propose to apply the same approach to color noise. Our implementation and training managed to reconstruct a noise-free output while conserving high-frequency details despite a loss of contrast. To address this issue, we designed a three-step pipeline using the contribution of a secondary denoiser to obtain high-quality results.
Description
CCS Concepts: Computing methodologies --> Ray tracing; Neural networks; Image processing
@inproceedings{10.2312:egp.20221011,
booktitle = {Eurographics 2022 - Posters},
editor = {Sauvage, Basile and Hasic-Telalovic, Jasminka},
title = {{Neural Denoising for Spectral Monte Carlo Rendering}},
author = {Rouphael, Robin and Noizet, Mathieu and Prévost, Stéphanie and Deleau, Hervé and Steffenel, Luiz-Angelo and Lucas, Laurent},
year = {2022},
publisher = {The Eurographics Association},
ISSN = {1017-4656},
ISBN = {978-3-03868-171-7},
DOI = {10.2312/egp.20221011}
}