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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