dc.contributor.author | Condor, Jorge | en_US |
dc.contributor.author | Jarabo, Adrián | en_US |
dc.contributor.editor | Ghosh, Abhijeet | en_US |
dc.contributor.editor | Wei, Li-Yi | en_US |
dc.date.accessioned | 2022-07-01T15:38:02Z | |
dc.date.available | 2022-07-01T15:38:02Z | |
dc.date.issued | 2022 | |
dc.identifier.isbn | 978-3-03868-187-8 | |
dc.identifier.issn | 1727-3463 | |
dc.identifier.uri | https://doi.org/10.2312/sr.20221155 | |
dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/sr20221155 | |
dc.description.abstract | We propose an efficient method for rendering complex luminaires using a high quality octree-based representation of the luminaire emission. Complex luminaires are a particularly challenging problem in rendering, due to their caustic light paths inside the luminaire. We reduce the geometric complexity of luminaires by using a simple proxy geometry, and encode the visuallycomplex emitted light field by using a neural radiance field. We tackle the multiple challenges of using NeRFs for representing luminaires, including their high dynamic range, high-frequency content and null-emission areas, by proposing a specialized loss function. For rendering, we distill our luminaires' NeRF into a plenoctree, which we can be easily integrated into traditional rendering systems. Our approach allows for speed-ups of up to 2 orders of magnitude in scenes containing complex luminaires introducing minimal error. | en_US |
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: Computer graphics --> Neural Rendering; Machine Learning --> Neural Radiance Fields | |
dc.subject | Computer graphics | |
dc.subject | Neural Rendering | |
dc.subject | Machine Learning | |
dc.subject | Neural Radiance Fields | |
dc.title | A Learned Radiance-Field Representation for Complex Luminaires | en_US |
dc.description.seriesinformation | Eurographics Symposium on Rendering | |
dc.description.sectionheaders | Neural Rendering | |
dc.identifier.doi | 10.2312/sr.20221155 | |
dc.identifier.pages | 49-58 | |
dc.identifier.pages | 10 pages | |