“Variance-aware multiple importance sampling” by Grittmann, Georgiev, Slusallek and Křivánek – ACM SIGGRAPH HISTORY ARCHIVES

“Variance-aware multiple importance sampling” by Grittmann, Georgiev, Slusallek and Křivánek

  • 2019 SA Technical Papers_Grittmann_Variance-aware multiple importance sampling

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    Variance-aware multiple importance sampling

Session/Category Title:   Light Transport

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Abstract:


    Many existing Monte Carlo methods rely on multiple importance sampling (MIS) to achieve robustness and versatility. Typically, the balance or power heuristics are used, mostly thanks to the seemingly strong guarantees on their variance. We show that these MIS heuristics are oblivious to the effect of certain variance reduction techniques like stratification. This shortcoming is particularly pronounced when unstratified and stratified techniques are combined (e.g., in a bidirectional path tracer). We propose to enhance the balance heuristic by injecting variance estimates of individual techniques, to reduce the variance of the combined estimator in such cases. Our method is simple to implement and introduces little overhead.

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