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dc.contributor.authorSolomon, Justin
dc.contributor.authorDe Goes, Fernando
dc.contributor.authorPeyré, Gabriel
HAL ID: 1211
dc.contributor.authorCuturi, Marco
HAL ID: 3354
dc.contributor.authorButscher, Adrian
dc.contributor.authorNguyen, Andy
dc.contributor.authorDu, Tao
dc.contributor.authorGuibas, Leonidas
dc.date.accessioned2016-07-06T14:34:38Z
dc.date.available2016-07-06T14:34:38Z
dc.date.issued2015
dc.identifier.issn0730-0301
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/15598
dc.language.isoenen
dc.subjectdisplacement interpolation
dc.subjectOptimal transportation
dc.subjectWasserstein distances
dc.subjectentropy
dc.subject.ddc519en
dc.titleConvolutional wasserstein distances: efficient optimal transportation on geometric domains
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherStanford University
dc.contributor.editoruniversityotherPixar Animation Studios
dc.contributor.editoruniversityotherKyoto University
dc.contributor.editoruniversityotherAutodesk Inc.
dc.description.abstractenThis paper introduces a new class of algorithms for optimization problems involving optimal transportation over geometric domains. Our main contribution is to show that optimal transportation can be made tractable over large domains used in graphics, such as images and triangle meshes, improving performance by orders of magnitude compared to previous work. To this end, we approximate optimal transportation distances using entropic regularization. The result- ing objective contains a geodesic distance-based kernel that can be approximated with the heat kernel. This approach leads to simple iterative numerical schemes with linear convergence, in which each iteration only requires Gaussian convolution or the solution of a sparse, pre-factored linear system. We demonstrate the versatility and efficiency of our method on tasks including reflectance interpolation, color transfer, and geometry processing.
dc.relation.isversionofjnlnameACM Transactions on Graphics
dc.relation.isversionofjnlvol34
dc.relation.isversionofjnlissue4
dc.relation.isversionofjnldate2015
dc.relation.isversionofjnlpagesart. 66
dc.relation.isversionofdoi10.1145/2766963
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-01188953
dc.relation.isversionofjnlpublisherAssociation for Computing Machinery
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2017-04-19T14:15:39Z


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