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Denoising Your Monte Carlo Renders: Recent Advances in Image-Space Adaptive Sampling and Reconstruction

SIGGRAPH 2015 Course

SIGGRAPH2015Course

Nima Kalantari
Fabrice Rousselle
Pradeep Sen
Sung-Eui Yoon
Matthias Zwicker 
 UC Santa Barbara
Disney Research
UC Santa Barbara
KAIST
University of Bern 

 

Abstract

With the ongoing shift in the computer graphics industry toward Monte Carlo rendering, there is a need for effective, practical noise-reduction techniques that are applicable to a wide range of rendering effects and easily integrated into existing production pipelines. This course surveys recent advances in image-space adaptive sampling and reconstruction algorithms for noise reduction, which have proven very effective at reducing the computational cost of Monte Carlo techniques in practice. These approaches leverage advanced image-filtering techniques with statistical methods for error estimation. They are attractive because they can be integrated easily into conventional Monte Carlo rendering frameworks, they are applicable to most rendering effects, and their computational overhead is modest.

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