Importance sampling w. 300 samples	Importance sampling w. 3000 samples	Structured sampling w. 300 samples	Structured sampling + sorting w. 4.7 rays/pixel

Abstract

We introduce structured importance sampling, a new technique for efficient ly rendering scenes illuminated by distant natural illumination given in an envi ronment map. Our method handles occlusion, high-frequency lighting, and it is significantly faster than alternative methods based on Monte Carlo sam pling. We achieve this speedup as a result of several ideas. First, we present a new metric for stratifying and sampling an environment map taking into account both the illumination intensity as well as the expected variance due to occlusion within the scene. We then present a novel hierarchical stratification algorithm that uses our metr ic to automatically stratify the environment map into regular strata. This appro ach enables a number of rendering optimizations, such as pre-integrating the illumination within each stratum to eliminate noise at the cost of adding bias, and sorting the strata to reduce the number of sample rays. We have render ed several scenes illuminated by natural lighting, and our results indicate that structured importance sampling is better than the best previous Monte Carlo techniques, requiring one to two orders of magnitude fewer samples for the same image quality.

Reference: Sameer Agarwal, Ravi Ramamoorthi, Serge Belongie, and Henrik Wann Jensen: "Structured Importance Sampling of Environment Maps". Proceedings of SIGGRAPH'2003.

structured.pdf (2.2MB)