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Denoising at scale for massive animated series

Published: 12 August 2018 Publication History

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.

Supplementary Material

MP4 File (29-429-boubekeur.mp4)
MP4 File (29-429.mp4)

References

[1]
Malik Boughida and Tamy Boubekeur. 2017. Bayesian Collaborative Denoising for Monte Carlo Rendering. Computer Graphics Forum (Proc. EGSR 2017) 36, 4 (2017), 137--153.
[2]
Malik Boughida and Tamy Boubekeur. 2017--2018. BCD: Bayesian Collaborative Denoiser for Monte-Carlo Rendering. https://github.com/superboubek/bcd/. (2017--2018).
[3]
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.
[4]
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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  1. Denoising at scale for massive animated series

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    cover image ACM Conferences
    SIGGRAPH '18: ACM SIGGRAPH 2018 Talks
    August 2018
    158 pages
    ISBN:9781450358200
    DOI:10.1145/3214745
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Published: 12 August 2018

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    Author Tags

    1. denoising
    2. monte carlo rendering
    3. path tracing

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