dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Vangorp, P. | |
dc.contributor.author | Fickel, Oscar | |
dc.date.accessioned | 2023-09-28T00:01:16Z | |
dc.date.available | 2023-09-28T00:01:16Z | |
dc.date.issued | 2023 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/45244 | |
dc.description.abstract | Spatiotemporal 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.sponsorship | Utrecht University | |
dc.language.iso | EN | |
dc.subject | We reimplement a paper on decorrelating ReSTIR via MCMC mutations and attempt to optimise it for a blue noise error distribution. | |
dc.title | Blue Noise Distributed MCMC Decorrelation of ReSTIR | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | computer graphics, ray tracing, path tracing, blue noise, ReSTIR, Markov chain, Metropolis-Hastings, resampling, rendering | |
dc.subject.courseuu | Game and Media Technology | |
dc.thesis.id | 24772 | |