Stochastic Lightcuts

dc.contributor.authorYuksel, Cemen_US
dc.contributor.editorSteinberger, Markus and Foley, Timen_US
dc.date.accessioned2019-07-11T06:52:36Z
dc.date.available2019-07-11T06:52:36Z
dc.date.issued2019
dc.description.abstractWe introduce stochastic lightcuts by combining the lighting approximation of lightcuts with stochastic sampling for efficiently rendering scenes with a large number of light sources. Our stochastic lightcuts method entirely eliminates the sampling correlation of lightcuts and replaces it with noise. To minimize this noise, we present a robust hierarchical sampling strategy, combining the benefits of importance sampling, adaptive sampling, and stratified sampling. Our approach also provides temporally stable results and lifts any restrictions on the light types that can be approximated with lightcuts. We present examples of using stochastic lightcuts with path tracing as well as indirect illumination with virtual lights, achieving more than an order of magnitude faster render times than lightcuts by effectively approximating direct illumination using a small number of light samples, in addition to providing temporal stability. Our comparisons to other stochastic sampling techniques demonstrate that we provide superior sampling quality that matches and improves the excellent convergence rates of the lightcuts approach.en_US
dc.description.sectionheadersDoing More With Each Ray
dc.description.seriesinformationHigh-Performance Graphics - Short Papers
dc.identifier.doi10.2312/hpg.20191192
dc.identifier.isbn978-3-03868-092-5
dc.identifier.issn2079-8687
dc.identifier.pages27-32
dc.identifier.urihttps://doi.org/10.2312/hpg.20191192
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/hpg20191192
dc.publisherThe Eurographics Associationen_US
dc.titleStochastic Lightcutsen_US
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