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Second-Order Approximation for Variance Reduction in Multiple Importance Sampling

hal.structure.identifierMelting the frontiers between Light, Shape and Matter [MANAO]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierLaboratoire Photonique, Numérique et Nanosciences [LP2N]
dc.contributor.authorLU, Heqi
hal.structure.identifierLaboratoire Photonique, Numérique et Nanosciences [LP2N]
hal.structure.identifierMelting the frontiers between Light, Shape and Matter [MANAO]
dc.contributor.authorPACANOWSKI, Romain
hal.structure.identifierMelting the frontiers between Light, Shape and Matter [MANAO]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierLaboratoire Photonique, Numérique et Nanosciences [LP2N]
dc.contributor.authorGRANIER, Xavier
dc.date.accessioned2023-05-12T10:44:53Z
dc.date.available2023-05-12T10:44:53Z
dc.date.issued2013-10-07
dc.identifier.issn0167-7055
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/181708
dc.description.abstractEnMonte Carlo Techniques are widely used in Computer Graphics to generate realistic images. Multiple Importance Sampling reduces the impact of choosing a dedicated strategy by balancing the number of samples between different strategies. However, an automatic choice of the optimal balancing remains a difficult problem. Without any scene characteristics knowledge, the default choice is to select the same number of samples from different strategies and to use them with heuristic techniques (e.g., balance, power or maximum). In this paper, we introduce a second-order approximation of variance for balance heuristic. Based on this approximation, we introduce an automatic distribution of samples for direct lighting without any prior knowledge of the scene characteristics. We demonstrate that for all our test scenes (with different types of materials, light sources and visibility complexity), our method actually reduces variance in average.We also propose an implementation with low overhead for online and GPU applications. We hope that this approach will help developing new balancing strategies.
dc.description.sponsorshipAnalyse des opérateurs de transport lumineux et applications - ANR-11-BS02-0006
dc.language.isoen
dc.publisherWiley
dc.subject.enMultiple Importance Sampling
dc.subject.enDirect Lighting
dc.subject.enImportance Sampling
dc.title.enSecond-Order Approximation for Variance Reduction in Multiple Importance Sampling
dc.typeArticle de revue
dc.identifier.doi10.1111/cgf.12220
dc.subject.halInformatique [cs]/Synthèse d'image et réalité virtuelle [cs.GR]
dc.subject.halInformatique [cs]/Modélisation et simulation
bordeaux.journalComputer Graphics Forum
bordeaux.page131-136
bordeaux.volume32
bordeaux.hal.laboratoriesLaboratoire Photonique, Numérique et Nanosciences (LP2N) - UMR 5298*
bordeaux.issue7
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-00878654
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00878654v1
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