Manifold Next Event Estimation

dc.contributor.authorHanika, Johannesen_US
dc.contributor.authorDroske, Marcen_US
dc.contributor.authorFascione, Lucaen_US
dc.contributor.editorJaakko Lehtinen and Derek Nowrouzezahraien_US
dc.date.accessioned2015-06-23T04:48:34Z
dc.date.available2015-06-23T04:48:34Z
dc.date.issued2015en_US
dc.description.abstractWe present manifold next event estimation (MNEE), a specialised technique for Monte Carlo light transport simulation to render refractive caustics by connecting surfaces to light sources (next event estimation) across transmissive interfaces. We employ correlated sampling by means of a perturbation strategy to explore all half vectors in the case of rough transmission while remaining outside of the context of Markov chain Monte Carlo, improving temporal stability. MNEE builds on differential geometry and manifold walks. It is very lightweight in its memory requirements, as it does not use light caching methods such as photon maps or importance sampling records. The method integrates seamlessly with existing Monte Carlo estimators via multiple importance sampling.en_US
dc.description.number4en_US
dc.description.sectionheadersLight Pathsen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume34en_US
dc.identifier.doi10.1111/cgf.12681en_US
dc.identifier.pages087-097en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12681en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.7 [Computer Graphics]en_US
dc.subjectThree Dimensional Graphics and Realismen_US
dc.subjectRaytracingen_US
dc.titleManifold Next Event Estimationen_US
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