“Practical Denoising for VFX Production Using Temporal Blur”
Conference:
Type(s):
Entry Number: 30
Title:
- Practical Denoising for VFX Production Using Temporal Blur
Presenter(s)/Author(s):
Abstract:
We present a simple, efficient, and reliable approach to denoising final ray traced renders during VFX production. Rather than seeking to remove all noise, we combine several simple steps that reduce noise dramatically. Our method has performed well on a wide variety of shows in Image Engine’s recent portfolio, including Game of Thrones Season 7, Lost in Space, and Thor: Ragnarok.
References:
Benedikt Bitterli, Fabrice Rousselle, Bochang Moon, José A. Iglesias-Guitián, David Adler, Kenny Mitchell, Wojciech Jarosz, and Jan Novák. 2016. Nonlinearly Weighted First-order Regression for Denoising Monte Carlo Renderings. Computer Graphics Forum 35, 4 (2016), 107–117.
Chakravarty R. Alla Chaitanya, Anton S. Kaplanyan, Christoph Schied, Marco Salvi, Aaron Lefohn, Derek Nowrouzezahrai, and Timo Aila. 2017. Interactive Reconstruction of Monte Carlo Image Sequences Using a Recurrent Denoising Autoencoder. ACM Trans. Graph. 36, 4, Article 98 (July 2017), 12 pages.
Luke Goddard. 2014. Silencing the Noise on Elysium. In ACM SIGGRAPH 2014 Talks (SIGGRAPH ’14). ACM, New York, NY, USA, Article 38, 1 pages.
Pixar Animation Studios. 2017. RenderMan : Denoise Workflow. (2017). https://rmanwiki.pixar.com/display/REN/Denoise+Workflow
