On-line Learning of Parametric Mixture Models for Light Transport Simulation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F14%3A10289478" target="_blank" >RIV/00216208:11320/14:10289478 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1145/2601097.2601203" target="_blank" >http://dx.doi.org/10.1145/2601097.2601203</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/2601097.2601203" target="_blank" >10.1145/2601097.2601203</a>

Result language
angličtina
Original language name
On-line Learning of Parametric Mixture Models for Light Transport Simulation
Original language description
Monte Carlo techniques for light transport simulation rely on importance sampling when constructing light transport paths. Previous work has shown that suitable sampling distributions can be recovered from particles distributed in the scene prior to rendering. We propose to represent the distributions by a parametric mixture model trained in an on-line (i.e. progressive) manner from a potentially infinite stream of particles. This enables recovering good sampling distributions in scenes with complex lighting, where the necessary number of particles may exceed available memory. Using these distributions for sampling scattering directions and light emission significantly improves the performance of state-of-the-art light transport simulation algorithms when dealing with complex lighting.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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