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.
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