Variance Reduction for Monte Carlo Rendering


Overview

Monte Carlo (MC) integration is a common technique for rendering images with distributed effects such as antialiasing, depth of field, motion blur, and global illumination. It simulates a variety of sophisticated light transport paths in a unified manner; it estimates pixel values by using stochastic point samples in the integral domain. Despite its generality and simplicity, however, the MC approach converges slowly and suffers from noisy images because of large variance. There are serveal categories of methods for variance reduction of MC rendering, such as filtering, importance sampling, caching and interpolation. Among them, we have developed methods following the paradigms of importance sampling and filtering and adaptive sampling.

Publications

SURE-based Optimization for Adaptive Sampling and Reconstruction
Tzu-Mao Li, Yu-Ting Wu, Yung-Yu Chuang
ACM SIGGRAPH Asia 2012
VisibilityCluster: Average Directional Visibility for Many-Light Rendering
Yu-Ting Wu, Yung-Yu Chuang
IEEE TVCG 2013
Dual-Matrix Sampling for Scalable Translucent Material Rendering
Yu-Ting Wu, Tzu-Mao Li, Yu-Hsun Lin, Yung-Yu Chuang
IEEE TVCG 2015


Support

This research is supported by:

cyy -a-t- csie.ntu.edu.tw