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

Sen, Pradeep; Zwicker, Matthias; Rousselle, Fabrice; Yoon, Sung-Eui; Kalantari, Nima Khademi (August 2015). Denoising Your Monte Carlo Renders: Recent Advances in Image Space-Adaptive Sampling and Reconstruction. In: ACM SIGGRAPH Tutorials. Los Angeles, CA. 09.-13.08.2015. 10.1145/2776880.2792740

Preview

Text a11-sen.pdf - Published Version Available under License Publisher holds Copyright. Download (120MB) | Preview

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.

Interest & Impact

Downloads

167 since deposited on 09 Jun 2016
37 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item