Correlation-Aware Multiple Importance Sampling for Bidirectional Rendering Algorithms

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dc.contributor.authorGrittmann, Pascalen_US
dc.contributor.authorGeorgiev, Iliyanen_US
dc.contributor.authorSlusallek, Philippen_US
dc.contributor.editorMitra, Niloy and Viola, Ivanen_US
dc.date.accessioned2021-04-09T08:00:27Z
dc.date.available2021-04-09T08:00:27Z
dc.date.issued2021
dc.description.abstractCombining diverse sampling techniques via multiple importance sampling (MIS) is key to achieving robustness in modern Monte Carlo light transport simulation. Many such methods additionally employ correlated path sampling to boost efficiency. Photon mapping, bidirectional path tracing, and path-reuse algorithms construct sets of paths that share a common prefix. This correlation is ignored by classical MIS heuristics, which can result in poor technique combination and noisy images.We propose a practical and robust solution to that problem. Our idea is to incorporate correlation knowledge into the balance heuristic, based on known path densities that are already required for MIS. This correlation-aware heuristic can achieve considerably lower error than the balance heuristic, while avoiding computational and memory overhead.en_US
dc.description.number2
dc.description.sectionheadersSampling Theory
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume40
dc.identifier.doi10.1111/cgf.142628
dc.identifier.issn1467-8659
dc.identifier.pages231-238
dc.identifier.urihttps://doi.org/10.1111/cgf.142628
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf142628
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectRendering
dc.subjectRay tracing
dc.subjectlight transport
dc.subjectbidirectional path tracing
dc.subjectVCM
dc.subjectmultiple importance sampling
dc.subjectpath correlation
dc.titleCorrelation-Aware Multiple Importance Sampling for Bidirectional Rendering Algorithmsen_US
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