Structured importance sampling of environment maps
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Structured importance sampling of environment maps. / Agarwal, Sameer; Ramamoorthi, Ravi; Belongie, Serge; Jensen, Henrik Wann.
In: ACM SIGGRAPH 2003 Papers, SIGGRAPH '03, 2003, p. 605-612.Research output: Contribution to journal › Conference article › Research › peer-review
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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 -
ID: 302055943