Product Importance Sampling for Light Transport Path Guiding
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F16%3A10336431" target="_blank" >RIV/00216208:11320/16:10336431 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1111/cgf.12950" target="_blank" >http://dx.doi.org/10.1111/cgf.12950</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1111/cgf.12950" target="_blank" >10.1111/cgf.12950</a>
Alternative languages
Result language
angličtina
Original language name
Product Importance Sampling for Light Transport Path Guiding
Original language description
The efficiency of Monte Carlo algorithms for light transport simulation is directly related to their ability to importance-sample the product of the illumination and reflectance in the rendering equation. Since the optimal sampling strategy would require knowledge about the transport solution itself, importance sampling most often follows only one of the known factors - BRDF or an approximation of the incident illumination. To address this issue, we propose to represent the illumination and the reflectance factors by the Gaussian mixture model (GMM), which we fit by using a combination of weighted expectation maximization and non-linear optimization methods. The GMM representation then allows us to obtain the resulting product distribution for importance sampling on-the-fly at each scene point. For its efficient evaluation and sampling we preform an up-front adaptive decimation of both factor mixtures. In comparison to state-of-the-art sampling methods, we show that our product importance sampling can lead to significantly better convergence in scenes with complex illumination and reflectance.
Czech name
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Czech description
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Classification
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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Result continuities
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)
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computer Graphics Forum [online]
ISSN
1467-8659
e-ISSN
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Volume of the periodical
35
Issue of the periodical within the volume
4
Country of publishing house
US - UNITED STATES
Number of pages
11
Pages from-to
67-77
UT code for WoS article
000383444100008
EID of the result in the Scopus database
2-s2.0-84983314451