Enhancing Spatiotemporal Resampling with a Novel MIS Weight

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dc.contributor.authorPan, Xingyueen_US
dc.contributor.authorZhang, Jiaxuanen_US
dc.contributor.authorHuang, Jiancongen_US
dc.contributor.authorLiu, Ligangen_US
dc.contributor.editorBermano, Amit H.en_US
dc.contributor.editorKalogerakis, Evangelosen_US
dc.date.accessioned2024-04-30T09:09:02Z
dc.date.available2024-04-30T09:09:02Z
dc.date.issued2024
dc.description.abstractIn real-time rendering, optimizing the sampling of large-scale candidates is crucial. The spatiotemporal reservoir resampling (ReSTIR) method provides an effective approach for handling large candidate samples, while the Generalized Resampled Importance Sampling (GRIS) theory provides a general framework for resampling algorithms. However, we have observed that when using the generalized multiple importance sampling (MIS) weight in previous work during spatiotemporal reuse, variances gradually amplify in the candidate domain when there are significant differences. To address this issue, we propose a new MIS weight suitable for resampling that blends samples from different sampling domains, ensuring convergence of results as the proportion of non-canonical samples increases. Additionally, we apply this weight to temporal resampling to reduce noise caused by scene changes or jitter. Our method effectively reduces energy loss in the biased version of ReSTIR DI while incurring no additional overhead, and it also suppresses artifacts caused by a high proportion of temporal samples. As a result, our approach leads to lower variance in the sampling results.en_US
dc.description.number2
dc.description.sectionheadersSampling and Image Enhancement
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume43
dc.identifier.doi10.1111/cgf.15049
dc.identifier.issn1467-8659
dc.identifier.pages13 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.15049
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15049
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies -> Rendering; Ray tracing; Monte Carlo algorithms
dc.subjectComputing methodologies
dc.subjectRendering
dc.subjectRay tracing
dc.subjectMonte Carlo algorithms
dc.titleEnhancing Spatiotemporal Resampling with a Novel MIS Weighten_US
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