Bibtex de la publication

@Article{ PaBaPaPo2011.1,
author = {Pajot, Anthony and Barthe, Loïc and Paulin, Mathias and Poulin, Pierre},
title = "{Representativity for Robust and Adaptive Multiple Importance Sampling}",
journal = {IEEE Transactions on Visualization and Computer Graphics},
publisher = {IEEE},
address = {},
year = {2011},
month = {août},
volume = {17},
number = {8},
pages = {1108--1121},
language = {anglais},
URL = {},
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 generalise our method to any integration problem and demonstrate that it can be used successfully to enhance robustness in different common rendering algorithms.}