What is it about?
Russian roulette and splitting are widely used techniques to increase the efficiency of Monte Carlo estimators. But, despite their popularity, there is little work on how to best apply them. Most existing approaches rely on simple heuristics based on, e.g., surface albedo and roughness. Their efficiency often hinges on user-controlled parameters. We instead iteratively learn optimal Russian roulette and splitting factors during rendering, using a simple and lightweight data structure. Given perfect estimates of variance and cost, our fixed-point iteration provably converges to the optimal Russian roulette and splitting factors that maximize the rendering efficiency. In our application to unidirectional path tracing, we achieve consistent and significant speed-ups over the state of the art.
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Read the Original
This page is a summary of: EARS, ACM Transactions on Graphics, July 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3528223.3530168.
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Resources
EARS: SIGGRAPH 2022 presentation
A quick 15-minute overview over "EARS: Efficiency-Aware Russian Roulette and Splitting" including background, a discussion of previous works, insights into our method and its implementation, as well as a short look at some of its results.
Paper website
Additional information and links.
Open Access version
The open access version of our paper.
Sourcecode
Our implementation on GitHub.
Contributors
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