--- res: bibo_abstract: - Multiple Importance Sampling (MIS) is a key technique for achieving robustness of Monte Carlo estimators in computer graphics and other fields. We derive optimal weighting functions for MIS that provably minimize the variance of an MIS estimator, given a set of sampling techniques. We show that the resulting variance reduction over the balance heuristic can be higher than predicted by the variance bounds derived by Veach and Guibas, who assumed only non-negative weights in their proof. We theoretically analyze the variance of the optimal MIS weights and show the relation to the variance of the balance heuristic. Furthermore, we establish a connection between the new weighting functions and control variates as previously applied to mixture sampling. We apply the new optimal weights to integration problems in light transport and show that they allow for new design considerations when choosing the appropriate sampling techniques for a given integration problem.@eng bibo_authorlist: - foaf_Person: foaf_givenName: Ivo foaf_name: Kondapaneni, Ivo foaf_surname: Kondapaneni - foaf_Person: foaf_givenName: Petr foaf_name: Vevoda, Petr foaf_surname: Vevoda - foaf_Person: foaf_givenName: Pascal foaf_name: Grittmann, Pascal foaf_surname: Grittmann - foaf_Person: foaf_givenName: Tomas foaf_name: Skrivan, Tomas foaf_surname: Skrivan foaf_workInfoHomepage: http://www.librecat.org/personId=486A5A46-F248-11E8-B48F-1D18A9856A87 - foaf_Person: foaf_givenName: Philipp foaf_name: Slusallek, Philipp foaf_surname: Slusallek - foaf_Person: foaf_givenName: Jaroslav foaf_name: Křivánek, Jaroslav foaf_surname: Křivánek bibo_doi: 10.1145/3306346.3323009 bibo_issue: '4' bibo_volume: 38 dct_date: 2019^xs_gYear dct_identifier: - UT:000475740600011 dct_isPartOf: - http://id.crossref.org/issn/0730-0301 dct_language: eng dct_publisher: ACM@ dct_title: Optimal multiple importance sampling@ ...