A spherical gaussian framework for bayesian Monte Carlo rendering of glossy surfaces
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/25139 |
Resumo: | The Monte Carlo method has proved to be very powerful to cope with global illumination problems but it remains costly in terms of sampling operations. In various applications, previous work has shown that Bayesian Monte Carlo can significantly outperform importance sampling Monte Carlo thanks to a more effective use of the prior knowledge and of the information brought by the samples set. These good results have been confirmed in the context of global illumination but strictly limited to the perfect diffuse case. Our main goal in this paper is to propose a more general Bayesian Monte Carlo solution that allows dealing with non-diffuse BRDFs thanks to a spherical Gaussian-based framework. We also propose a fast hyperparameters determination method which avoids learning the hyperparameters for each BRDF. These contributions represent two major steps towards generalizing Bayesian Monte Carlo for global illumination rendering. We show that we achieve substantial quality improvements over importance sampling at comparable computational cost. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
spelling |
A spherical gaussian framework for bayesian Monte Carlo rendering of glossy surfacesBayesian Monte CarloGaussian processSpherical gaussianScience & TechnologyThe Monte Carlo method has proved to be very powerful to cope with global illumination problems but it remains costly in terms of sampling operations. In various applications, previous work has shown that Bayesian Monte Carlo can significantly outperform importance sampling Monte Carlo thanks to a more effective use of the prior knowledge and of the information brought by the samples set. These good results have been confirmed in the context of global illumination but strictly limited to the perfect diffuse case. Our main goal in this paper is to propose a more general Bayesian Monte Carlo solution that allows dealing with non-diffuse BRDFs thanks to a spherical Gaussian-based framework. We also propose a fast hyperparameters determination method which avoids learning the hyperparameters for each BRDF. These contributions represent two major steps towards generalizing Bayesian Monte Carlo for global illumination rendering. We show that we achieve substantial quality improvements over importance sampling at comparable computational cost.Fundação para a Ciência e a Tecnologia (FCT) within project PEst-OE/EEI/UI0752/2011IEEEUniversidade do MinhoMarques, RicardoBouville, ChristianRibardiére, MichaelSantos, Luís PauloBouatouch, Kadi2013-05-132013-05-13T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/25139eng1077-262610.1109/TVCG.2013.79http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6514875info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:07:26Zoai:repositorium.sdum.uminho.pt:1822/25139Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:58:25.156971Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A spherical gaussian framework for bayesian Monte Carlo rendering of glossy surfaces |
title |
A spherical gaussian framework for bayesian Monte Carlo rendering of glossy surfaces |
spellingShingle |
A spherical gaussian framework for bayesian Monte Carlo rendering of glossy surfaces Marques, Ricardo Bayesian Monte Carlo Gaussian process Spherical gaussian Science & Technology |
title_short |
A spherical gaussian framework for bayesian Monte Carlo rendering of glossy surfaces |
title_full |
A spherical gaussian framework for bayesian Monte Carlo rendering of glossy surfaces |
title_fullStr |
A spherical gaussian framework for bayesian Monte Carlo rendering of glossy surfaces |
title_full_unstemmed |
A spherical gaussian framework for bayesian Monte Carlo rendering of glossy surfaces |
title_sort |
A spherical gaussian framework for bayesian Monte Carlo rendering of glossy surfaces |
author |
Marques, Ricardo |
author_facet |
Marques, Ricardo Bouville, Christian Ribardiére, Michael Santos, Luís Paulo Bouatouch, Kadi |
author_role |
author |
author2 |
Bouville, Christian Ribardiére, Michael Santos, Luís Paulo Bouatouch, Kadi |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Marques, Ricardo Bouville, Christian Ribardiére, Michael Santos, Luís Paulo Bouatouch, Kadi |
dc.subject.por.fl_str_mv |
Bayesian Monte Carlo Gaussian process Spherical gaussian Science & Technology |
topic |
Bayesian Monte Carlo Gaussian process Spherical gaussian Science & Technology |
description |
The Monte Carlo method has proved to be very powerful to cope with global illumination problems but it remains costly in terms of sampling operations. In various applications, previous work has shown that Bayesian Monte Carlo can significantly outperform importance sampling Monte Carlo thanks to a more effective use of the prior knowledge and of the information brought by the samples set. These good results have been confirmed in the context of global illumination but strictly limited to the perfect diffuse case. Our main goal in this paper is to propose a more general Bayesian Monte Carlo solution that allows dealing with non-diffuse BRDFs thanks to a spherical Gaussian-based framework. We also propose a fast hyperparameters determination method which avoids learning the hyperparameters for each BRDF. These contributions represent two major steps towards generalizing Bayesian Monte Carlo for global illumination rendering. We show that we achieve substantial quality improvements over importance sampling at comparable computational cost. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-05-13 2013-05-13T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/25139 |
url |
http://hdl.handle.net/1822/25139 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1077-2626 10.1109/TVCG.2013.79 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6514875 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132374707994624 |