A spherical Gaussian framework for Bayesian Monte Carlo rendering of glossy surfaces

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 m...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1996. - 19(2013), 10 vom: 08. Okt., Seite 1619-32
1. Verfasser: Marques, Ricardo (VerfasserIn)
Weitere Verfasser: Bouville, Christian, Ribardière, Mickaël, Santos, Luís Paulo, Bouatouch, Kadi
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2013
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article Research Support, Non-U.S. Gov't
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520 |a 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 nondiffuse BRDFs thanks to a spherical Gaussian-based framework. We also propose a fast hyperparameters determination method that avoids learning the hyperparameters for each BRDF. These contributions represent two major steps toward 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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650 4 |a Research Support, Non-U.S. Gov't 
700 1 |a Bouville, Christian  |e verfasserin  |4 aut 
700 1 |a Ribardière, Mickaël  |e verfasserin  |4 aut 
700 1 |a Santos, Luís Paulo  |e verfasserin  |4 aut 
700 1 |a Bouatouch, Kadi  |e verfasserin  |4 aut 
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