Sampling of Anisotropic Spatial Gaussians for Path Guiding
| dc.contributor.author | Lelyakin, Sergey | en_US |
| dc.contributor.author | Schüßler, Vincent | en_US |
| dc.contributor.author | Dachsbacher, Carsten | en_US |
| dc.contributor.editor | Günther, Tobias | en_US |
| dc.contributor.editor | Montazeri, Zahra | en_US |
| dc.date.accessioned | 2025-05-09T09:31:07Z | |
| dc.date.available | 2025-05-09T09:31:07Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Directional models in path guiding struggle with representing parallax effects or anisotropic features. Our model instead describes the spatial distribution of a target vertex using a 3D Gaussian mixture model. While this dispenses with the need for reprojection and allows to represent anisotropic features easily, its directional probability density is not readily available, since it involves a marginal integral. In this work, we derive an expression for the PDF of our model in solid angle measure that is practical to evaluate. We demonstrate how our model can improve guiding accuracy in various scenes. | en_US |
| dc.description.sectionheaders | Posters | |
| dc.description.seriesinformation | Eurographics 2025 - Posters | |
| dc.identifier.doi | 10.2312/egp.20251017 | |
| dc.identifier.isbn | 978-3-03868-269-1 | |
| dc.identifier.issn | 1017-4656 | |
| dc.identifier.pages | 2 pages | |
| dc.identifier.uri | https://doi.org/10.2312/egp.20251017 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/egp20251017 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Computing methodologies → Rendering; Ray tracing | |
| dc.subject | Computing methodologies → Rendering | |
| dc.subject | Ray tracing | |
| dc.title | Sampling of Anisotropic Spatial Gaussians for Path Guiding | en_US |