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Multiple importance sampling characterization by weighted mean invariance
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DOI：10.1007/s00371-018-1522-x.png)
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Abstract
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In this paper, we examine the linear combination of techniques and multiple importance sampling for Monte Carlo integration from a new perspective of quasi-arithmetic weighted means. The invariance property of these means allows us to define a new family of heuristics. We illustrate our results with several rendering examples, including environment mapping and path tracing.
Keywords:
Global illumination
Rendering equation analysis
Multiple importance sampling
Monte Carlo
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