title: Representativity for robust and adaptive multiple importance sampling.
abstract: We present a general method enhancing the robustness of estimators based on multiple importance sampling (MIS) in a numerical integration context. MIS minimizes variance of estimators for a given sampling configuration, but when this configuration is less adapted to the integrand, the resulting estimator suffers from extra variance. We address this issue by introducing the notion of "representativity" of a sampling strategy, and demonstrate how it can be used to increase robustness of estimators, by adapting them to the integrand. We first show how to compute representativities using common rendering informations such as BSDF, photon maps, or caches in order to choose the best sampling strategy for MIS. We then give hints to generalize our method to any integration problem and demonstrate that it can be used successfully to enhance robustness in different common rendering algorithms.
authors.affiliation: IRIT, Université Paul Sabatier IRIT-CNRS, UMR 5505, 31062 Toulouse Cedex 09, France. anthony.pajot@irit.fr
authors.forename: Anthony
authors.initials: A
authors.lastname: Pajot
authors.forename: Loïc
authors.initials: L
authors.lastname: Barthe
authors.forename: Mathias
authors.initials: M
authors.lastname: Paulin
authors.forename: Pierre
authors.initials: P
authors.lastname: Poulin