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

Nima Kalantari1 Fabrice Rousselle2 Pradeep Sen3 Sung-Eui Yoon4 Matthias Zwicker5
1Texas A& M University 2Disney Research, Zurich 3University of California, Santa Barbara 4KAIST 5University of Maryland, College Park

SIGGRAPH 2015 Course

Teaser Left: Noisy Monte Carlo render due to limited computation time. Right: denoised result at equal computation time.


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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