Sequential Monte Carlo Adaptation in Low-Anisotropy Participating Media

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dc.contributor.authorPegoraro, Vincenten_US
dc.contributor.authorWald, Ingoen_US
dc.contributor.authorParker, Steven G.en_US
dc.date.accessioned2015-02-21T17:05:41Z
dc.date.available2015-02-21T17:05:41Z
dc.date.issued2008en_US
dc.description.abstractThis paper presents a novel method that effectively combines both control variates and importance sampling in a sequential Monte Carlo context. The radiance estimates computed during the rendering process are cached in a 5D adaptive hierarchical structure that defines dynamic predicate functions for both variance reduction techniques and guarantees well-behaved PDFs, yielding continually increasing efficiencies thanks to a marginal computational overhead. While remaining unbiased, the technique is effective within a single pass as both estimation and caching are done online, exploiting the coherency in illumination while being independent of the actual scene representation. The method is relatively easy to implement and to tune via a single parameter, and we demonstrate its practical benefits with important gains in convergence rate and competitive results with state of the art techniques.en_US
dc.description.number4en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume27en_US
dc.identifier.doi10.1111/j.1467-8659.2008.01247.xen_US
dc.identifier.issn1467-8659en_US
dc.identifier.pages1097-1104en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2008.01247.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltden_US
dc.titleSequential Monte Carlo Adaptation in Low-Anisotropy Participating Mediaen_US
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27-Issue 4
EGSR08: 19th Eurographics Symposium on Rendering