On Learning the Best Local Balancing Strategy

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dc.contributor.authorMurray, Daviden_US
dc.contributor.authorBenzait, Sofianeen_US
dc.contributor.authorPacanowski, Romainen_US
dc.contributor.authorGranier, Xavieren_US
dc.contributor.editorWilkie, Alexander and Banterle, Francescoen_US
dc.date.accessioned2020-05-24T13:42:28Z
dc.date.available2020-05-24T13:42:28Z
dc.date.issued2020
dc.description.abstractFast computation of light propagation using Monte Carlo techniques requires finding the best samples from the space of light paths. For the last 30 years, numerous strategies have been developed to address this problem but choosing the best one is really scene-dependent. Multiple Importance Sampling (MIS) emerges as a potential generic solution by combining different weighted strategies, to take advantage of the best ones. Most recent work have focused on defining the best weighting scheme. Among them, two paper have shown that it is possible, in the context of direct illumination, to estimate the best way to balance the number of samples between two strategies, on a per-pixel basis. In this paper, we extend this previous approach to Global Illumination and to three strategies.en_US
dc.description.sectionheadersRendering II + Shape
dc.description.seriesinformationEurographics 2020 - Short Papers
dc.identifier.doi10.2312/egs.20201009
dc.identifier.isbn978-3-03868-101-4
dc.identifier.issn1017-4656
dc.identifier.pages25-28
dc.identifier.urihttps://doi.org/10.2312/egs.20201009
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egs20201009
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/]
dc.titleOn Learning the Best Local Balancing Strategyen_US
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