Optimal Deterministic Mixture Sampling
| dc.contributor.author | Sbert, Mateu | en_US |
| dc.contributor.author | Havran, Vlastimil | en_US |
| dc.contributor.author | Szirmay-Kalos, László | en_US |
| dc.contributor.editor | Cignoni, Paolo and Miguel, Eder | en_US |
| dc.date.accessioned | 2019-05-05T17:50:17Z | |
| dc.date.available | 2019-05-05T17:50:17Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Multiple Importance Sampling (MIS) can combine several sampling techniques preserving their advantages. For example, we can consider different Monte Carlo rendering methods generating light path samples proportionally only to certain factors of the integrand. MIS then becomes equivalent to the application of the mixture of individual sampling densities, thus can simultaneously mimic the densities of all considered techniques. The weights of the mixture sampling depends on how many samples are generated with each particular method. This paper examines the optimal determination of this parameter. The proposed method is demonstrated with the combination of BRDF sampling and Light source sampling, and we show that it not only outperforms the application of the two individual methods, but is superior to other recent combination strategies and is close to the theoretical optimum. | en_US |
| dc.description.sectionheaders | Rendering | |
| dc.description.seriesinformation | Eurographics 2019 - Short Papers | |
| dc.identifier.doi | 10.2312/egs.20191018 | |
| dc.identifier.issn | 1017-4656 | |
| dc.identifier.pages | 73-76 | |
| dc.identifier.uri | https://doi.org/10.2312/egs.20191018 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/egs20191018 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.title | Optimal Deterministic Mixture Sampling | en_US |
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