TY - GEN

T1 - Structured importance sampling of environment maps

AU - Agarwal, Sameer

AU - Ramamoorthi, Ravi

AU - Belongie, Serge

AU - Jensen, Henrik Wann

PY - 2003

Y1 - 2003

N2 - We introduce structured importance sampling, a new technique for efficiently rendering scenes illuminated by distant natural illumination given in an environment map. Our method handles occlusion, high-frequency lighting, and is significantly faster than alternative methods based on Monte Carlo sampling. 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 metric to automatically stratify the environment map into regular strata. This approach 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 rendered 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.

AB - We introduce structured importance sampling, a new technique for efficiently rendering scenes illuminated by distant natural illumination given in an environment map. Our method handles occlusion, high-frequency lighting, and is significantly faster than alternative methods based on Monte Carlo sampling. 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 metric to automatically stratify the environment map into regular strata. This approach 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 rendered 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.

KW - environment mapping

KW - global illumination

KW - illumination

KW - image synthesis

KW - Monte Carlo techniques

KW - ray tracing

KW - rendering

KW - shadow algorithms

UR - http://www.scopus.com/inward/record.url?scp=77954014189&partnerID=8YFLogxK

U2 - 10.1145/1201775.882314

DO - 10.1145/1201775.882314

M3 - Conference article

AN - SCOPUS:77954014189

SP - 605

EP - 612

JO - ACM SIGGRAPH 2003 Papers, SIGGRAPH '03

JF - ACM SIGGRAPH 2003 Papers, SIGGRAPH '03

T2 - ACM SIGGRAPH 2003 Papers, SIGGRAPH '03

Y2 - 27 July 2003 through 31 July 2003

ER -