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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorVangorp, P.
dc.contributor.authorFickel, Oscar
dc.date.accessioned2023-09-28T00:01:16Z
dc.date.available2023-09-28T00:01:16Z
dc.date.issued2023
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/45244
dc.description.abstractSpatiotemporal resampling (ReSTIR) [Bitterli et al., 2020; Lin et al., 2021] is a popular new ray tracing technique. Unfortunately it can suffer from correlation artifacts if left unchecked. One solution for this is offered by Sawhney et al. [2022] in the form of Markov Chain Monte Carlo mutations. We reimplement and evaluate their proposed algorithm, and attempt to optimise it for blue noise. Our addition of blue noise mutations is unsuccessful, but still provides some insight into how the underlying characteristics of decorrelated ReSTIR work against a simple solution for achieving blue noise.
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectWe reimplement a paper on decorrelating ReSTIR via MCMC mutations and attempt to optimise it for a blue noise error distribution.
dc.titleBlue Noise Distributed MCMC Decorrelation of ReSTIR
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordscomputer graphics, ray tracing, path tracing, blue noise, ReSTIR, Markov chain, Metropolis-Hastings, resampling, rendering
dc.subject.courseuuGame and Media Technology
dc.thesis.id24772


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