Single-pass Stratified Importance Resampling

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dc.contributor.authorCiklabakkal, Egeen_US
dc.contributor.authorGruson, Adrienen_US
dc.contributor.authorGeorgiev, Iliyanen_US
dc.contributor.authorNowrouzezahrai, Dereken_US
dc.contributor.authorHachisuka, Toshiyaen_US
dc.contributor.editorGhosh, Abhijeeten_US
dc.contributor.editorWei, Li-Yien_US
dc.date.accessioned2022-07-01T15:36:41Z
dc.date.available2022-07-01T15:36:41Z
dc.date.issued2022
dc.description.abstractResampling is the process of selecting from a set of candidate samples to achieve a distribution (approximately) proportional to a desired target. Recent work has revisited its application to Monte Carlo integration, yielding powerful and practical importance sampling methods. One drawback of existing resampling methods is that they cannot generate stratified samples. We propose two complementary techniques to achieve efficient stratified resampling. We first introduce bidirectional CDF sampling which yields the same result as conventional inverse CDF sampling but in a single pass over the candidates, without needing to store them, similarly to reservoir sampling. We then order the candidates along a space-filling curve to ensure that stratified CDF sampling of candidate indices yields stratified samples in the integration domain. We showcase our method on various resampling-based rendering problems.en_US
dc.description.number4
dc.description.sectionheadersSampling
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume41
dc.identifier.doi10.1111/cgf.14585
dc.identifier.issn1467-8659
dc.identifier.pages41-49
dc.identifier.pages9 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14585
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14585
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies --> Rendering
dc.subjectComputing methodologies
dc.subjectRendering
dc.titleSingle-pass Stratified Importance Resamplingen_US
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