Improving global exploration of MCMC light transport simulation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F16%3A10336440" target="_blank" >RIV/00216208:11320/16:10336440 - isvavai.cz</a>
Result on the web
<a href="http://dl.acm.org/citation.cfm?id=2945128" target="_blank" >http://dl.acm.org/citation.cfm?id=2945128</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/2945078.2945128" target="_blank" >10.1145/2945078.2945128</a>

Result language
angličtina
Original language name
Improving global exploration of MCMC light transport simulation
Original language description
Markov Chain Monte Carlo (MCMC) has recently received a lot of attention in light transport simulation research [Hanika et al. 2015; Hachisuka et al. 2014]. While these methods aim at high quality sampling of local extremes of the path space (so called local exploration), the other issue - discovering these extremes - has been so far neglected. Poor global exploration results in oversampling some parts of the paths space, while undersampling or completely missing other parts (see Fig. 1). Such behavior of MCMC-based light transport algorithms limits their use in practice, since we can never tell for sure whether the image has already converged.
Czech name
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Czech description
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Project
<a href="/en/project/GA16-18964S" target="_blank" >GA16-18964S: Adaptive sampling and Markov chain Monte Carlo methods in light transport simulation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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