“Denoising at Scale for Massive Animated Series” by Watamori, Ozai and Hasegawa
Conference:
Type:
Entry Number: 29
Title:
- Denoising at Scale for Massive Animated Series
Presenter(s)/Author(s):
Abstract:
In the modern era of physically-based shading, removing the substantial amount of high frequency noise produced by Monte Carlo rendering techniques is a key challenge for production renderers. Beyond the recent advances in sample-based and feature-based denoising, production constraints and scale introduce additional mandatory features for candidate denoisers. In this talk, we discuss how denoising is deployed in Shining, the production renderer developed by Ubisoft Motion Pictures for the Rabbids Invasion animated TV series. The scale of the show, as well as the required control for artists, led us to the integration of a sample-based denoiser, which enables per-AOV denoising control, with a minimum overhead regarding engine integration and production workflow. As a result, all-effects denoising is made possible for the new TV series season and proved useful in numerous lighting and material scenarios. At the core of the denoising pipeline, our BCD algorithm, recently made open source, provides a robust and fast mechanism to filter out Monte Carlo noise while retaining features, for complex lighting and viewing conditions, with trivial per-AOV setup.
References:
Malik Boughida and Tamy Boubekeur. 2017. Bayesian Collaborative Denoising for Monte Carlo Rendering. Computer Graphics Forum (Proc. EGSR 2017) 36, 4 (2017), 137–153.
Malik Boughida and Tamy Boubekeur. 2017–2018. BCD: Bayesian Collaborative Denoiser for Monte-Carlo Rendering. https://github.com/superboubek/bcd/. (2017–2018).
Mauricio Delbracio, Pablo Musé, Antoni Buades, Julien Chauvier, Nicholas Phelps, and Jean-Michel Morel. 2014. Boosting Monte Carlo Rendering by Ray Histogram Fusion. ACM Transactions on Graphics 33, 1, Article 8 (2014), 8:1–8:15 pages.
M. Lebrun, A. Buades, and J. M. Morel. 2013. A Nonlocal Bayesian Image Denoising Algorithm. SIAM Journal on Imaging Sciences 6, 3 (2013), 1665–1688.
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Acknowledgements:
This work is partially supported by the French Agence Nationale pour la Recherche under grant ANR 16-LCV2-0009-01 ALLEGORI and by BPI France, under grant PAPAYA.
