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A median cut algorithm for light probe sampling
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Massive model visualization using realtime ray tracing
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EGSR'05 Proceedings of the Sixteenth Eurographics conference on Rendering Techniques
Importance resampling for global illumination
EGSR'05 Proceedings of the Sixteenth Eurographics conference on Rendering Techniques
Bidirectional importance sampling for direct illumination
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Interactive illumination with coherent shadow maps
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Incremental instant radiosity for real-time indirect illumination
EGSR'07 Proceedings of the 18th Eurographics conference on Rendering Techniques
Two stage importance sampling for direct lighting
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Sequential sampling for dynamic environment map illumination
EGSR'06 Proceedings of the 17th Eurographics conference on Rendering Techniques
Real-time kd-tree based importance sampling of environment maps
Proceedings of the 28th Spring Conference on Computer Graphics
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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.