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5D Covariance Tracing for Efficient Defocus and Motion Blur

hal.structure.identifierLaboratoire Photonique, Numérique et Nanosciences [LP2N]
hal.structure.identifierModels and Algorithms for Visualization and Rendering [MAVERICK]
dc.contributor.authorBELCOUR, Laurent
hal.structure.identifierModels and Algorithms for Visualization and Rendering [MAVERICK]
dc.contributor.authorSOLER, Cyril
hal.structure.identifierUniversity College of London [London] [UCL]
dc.contributor.authorSUBR, Kartic
hal.structure.identifierModels and Algorithms for Visualization and Rendering [MAVERICK]
dc.contributor.authorHOLZSCHUCH, Nicolas
hal.structure.identifierComputer Science and Artificial Intelligence Laboratory [Cambridge] [CSAIL]
dc.contributor.authorDURAND, Frédo
dc.date.accessioned2023-05-12T10:55:57Z
dc.date.available2023-05-12T10:55:57Z
dc.date.issued2013-06-01
dc.identifier.issn0730-0301
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/181942
dc.description.abstractEnThe 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.isoen
dc.publisherAssociation for Computing Machinery
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.title.en5D Covariance Tracing for Efficient Defocus and Motion Blur
dc.typeArticle de revue
dc.identifier.doi10.1145/2487228.2487239
dc.subject.halInformatique [cs]/Synthèse d'image et réalité virtuelle [cs.GR]
bordeaux.journalACM Transactions on Graphics
bordeaux.pageArticle No. 31
bordeaux.volume32
bordeaux.hal.laboratoriesLaboratoire Photonique, Numérique et Nanosciences (LP2N) - UMR 5298*
bordeaux.issue3
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-00814164
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00814164v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=ACM%20Transactions%20on%20Graphics&rft.date=2013-06-01&rft.volume=32&rft.issue=3&rft.spage=Article%20No.%2031&rft.epage=Article%20No.%2031&rft.eissn=0730-0301&rft.issn=0730-0301&rft.au=BELCOUR,%20Laurent&SOLER,%20Cyril&SUBR,%20Kartic&HOLZSCHUCH,%20Nicolas&DURAND,%20Fr%C3%A9do&rft.genre=article


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