Convergence Estimation of Markov-Chain Monte Carlo Rendering

dc.contributor.authorYu, Ruien_US
dc.contributor.authorSun, Guangzhongen_US
dc.contributor.authorZhao, Shuangen_US
dc.contributor.authorDong, Yueen_US
dc.contributor.editorWang, Beibeien_US
dc.contributor.editorWilkie, Alexanderen_US
dc.date.accessioned2025-06-20T07:48:27Z
dc.date.available2025-06-20T07:48:27Z
dc.date.issued2025
dc.description.abstractWe present a theoretical framework for estimating the convergence of Markov-Chain Monte Carlo (MCMC) rendering algorithms. Our theory considers both the variance and the correlation between samples, allowing for quantitative analyses of the convergence properties of MCMC estimators. With our theoretical framework, we devise a Monte Carlo (MC) algorithm capable of accurately estimating the expected MSE of an MCMC rendering algorithm. By adopting an efficient rejection sampling scheme, our MC-based MSE estimator yields a lower standard deviation compared to directly measuring the MSE by running the MCMC rendering algorithm multiple times. Moreover, we demonstrate that modifying the target distribution of the Markov chain by roughening the specular BRDF might lead to faster convergence on some scenarios. This finding suggests that our estimator can serve as a potential guide for selecting the target distribution.en_US
dc.description.sectionheadersSampling and Guiding
dc.description.seriesinformationEurographics Symposium on Rendering
dc.identifier.doi10.2312/sr.20251179
dc.identifier.isbn978-3-03868-292-9
dc.identifier.issn1727-3463
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.2312/sr.20251179
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/sr20251179
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies -> Ray tracing
dc.subjectComputing methodologies
dc.subjectRay tracing
dc.titleConvergence Estimation of Markov-Chain Monte Carlo Renderingen_US
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