Multiple Importance Sampling Characterization by Weighted Mean Invariance

Mateu Sbert Vlastimil Havran László Szirmay-Kalos Victor Elvira
The Visual Computer 34(6-8):843-852, 2018
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.