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

Courses

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

Tuesday, 11 August 2:00 PM - 3:30 PM | Los Angeles Convention Center, Room 404AB


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

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.

Pradeep Sen
University of California, Santa Barbara

Mathias Zwicker
University of Bern

Fabrice Rousselle
Disney Research

Sung-Eui Yoon
Korea Advanced Institute of Science and Technology

Nima Khademi Kalantari
University of California, Santa Barbara