Bayesian and Quasi Monte Carlo Spherical Integration for Illumination Integrals
| dc.contributor.author | Marques, Ricardo | en_US |
| dc.contributor.author | Bouville, Christian | en_US |
| dc.contributor.author | Bouatouch, Kadi | en_US |
| dc.contributor.editor | Nicolas Holzschuch and Karol Myszkowski | en_US |
| dc.date.accessioned | 2014-12-16T07:13:49Z | |
| dc.date.available | 2014-12-16T07:13:49Z | |
| dc.date.issued | 2014 | en_US |
| dc.description.abstract | The spherical sampling of the incident radiance function entails a high computational cost. Therefore the illumination integral must be evaluated using a limited set of samples. Such a restriction raises the question of how to obtain the most accurate approximation possible with such a limited set of samples. We need to ensure that sampling produces the highest amount of information possible by carefully placing the limited set of samples. Furthermore we want our integral evaluation to take into account not only the information produced by the sampling but also possible information available prior to sampling. In this tutorial we focus on the case of hemispherical sampling for spherical Monte Carlo (MC) integration. We will show that existing techniques can be improved by making a detailed analysis of the theory of MC spherical integration. We will then use this theory to identify and improve the weak points of current approaches, based on very recent advances in the fields of integration and spherical Quasi-Monte Carlo integration. | en_US |
| dc.description.seriesinformation | Eurographics 2014 - Tutorials | en_US |
| dc.identifier.issn | 1017-4656 | en_US |
| dc.identifier.uri | https://doi.org/10.2312/egt.20141020 | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.title | Bayesian and Quasi Monte Carlo Spherical Integration for Illumination Integrals | en_US |
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