5D Covariance Tracing for Efficient Defocus and Motion Blur
hal.structure.identifier | Laboratoire Photonique, Numérique et Nanosciences [LP2N] | |
hal.structure.identifier | Models and Algorithms for Visualization and Rendering [MAVERICK] | |
dc.contributor.author | BELCOUR, Laurent | |
hal.structure.identifier | Models and Algorithms for Visualization and Rendering [MAVERICK] | |
dc.contributor.author | SOLER, Cyril | |
hal.structure.identifier | University College of London [London] [UCL] | |
dc.contributor.author | SUBR, Kartic | |
hal.structure.identifier | Models and Algorithms for Visualization and Rendering [MAVERICK] | |
dc.contributor.author | HOLZSCHUCH, Nicolas | |
hal.structure.identifier | Computer Science and Artificial Intelligence Laboratory [Cambridge] [CSAIL] | |
dc.contributor.author | DURAND, Frédo | |
dc.date.accessioned | 2023-05-12T10:55:57Z | |
dc.date.available | 2023-05-12T10:55:57Z | |
dc.date.issued | 2013-06-01 | |
dc.identifier.issn | 0730-0301 | |
dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/181942 | |
dc.description.abstractEn | The rendering of effects such as motion blur and depth-of-field requires costly 5D integrals. We dramatically accelerate their computation through adaptive sampling and reconstruction based on the prediction of the anisotropy and bandwidth of the integrand. For this, we develop a new frequency analysis of the 5D temporal light-field, and show that first-order motion can be handled through simple changes of coordinates in 5D. We further introduce a compact representation of the spectrum using the covariance matrix and Gaussian approximations. We derive update equations for the 5 by 5 covariance matrices for each atomic light transport event, such as transport, occlusion, BRDF, texture, lens, and motion. The focus on atomic operations makes our work general, and removes the need for special-case formulas. We present a new rendering algorithm that computes 5D covariance matrices on the image plane by tracing paths through the scene, focusing on the single-bounce case. This allows us to reduce sampling rates when appropriate and perform reconstruction of images with complex depth-of-field and motion blur effects. | |
dc.language.iso | en | |
dc.publisher | Association for Computing Machinery | |
dc.rights.uri | http://creativecommons.org/licenses/by/ | |
dc.title.en | 5D Covariance Tracing for Efficient Defocus and Motion Blur | |
dc.type | Article de revue | |
dc.identifier.doi | 10.1145/2487228.2487239 | |
dc.subject.hal | Informatique [cs]/Synthèse d'image et réalité virtuelle [cs.GR] | |
bordeaux.journal | ACM Transactions on Graphics | |
bordeaux.page | Article No. 31 | |
bordeaux.volume | 32 | |
bordeaux.hal.laboratories | Laboratoire Photonique, Numérique et Nanosciences (LP2N) - UMR 5298 | * |
bordeaux.issue | 3 | |
bordeaux.institution | Université de Bordeaux | |
bordeaux.institution | CNRS | |
bordeaux.peerReviewed | oui | |
hal.identifier | hal-00814164 | |
hal.version | 1 | |
hal.origin.link | https://hal.archives-ouvertes.fr//hal-00814164v1 | |
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