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Feature Preserving Point Set Surfaces based on Non-Linear Kernel Regression

hal.structure.identifierEidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] [ETH Zürich]
dc.contributor.authorOZTIRELI, Cengiz
hal.structure.identifierCNR Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo” [Pisa] [CNR | ISTI]
hal.structure.identifierVisualization and manipulation of complex data on wireless mobile devices [IPARLA ]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorGUENNEBAUD, Gaël
hal.structure.identifierEidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] [ETH Zürich]
dc.contributor.authorGROSS, Markus
dc.date.accessioned2024-04-15T09:52:32Z
dc.date.available2024-04-15T09:52:32Z
dc.date.issued2009
dc.identifier.issn0167-7055
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/198525
dc.description.abstractEnMoving least squares (MLS) is a very attractive tool to design effective meshless surface representations. However, as long as approximations are performed in a least square sense, the resulting definitions remain sensitive to outliers, and smooth-out small or sharp features. In this paper, we address these major issues, and present a novel point based surface definition combining the simplicity of implicit MLS surfaces [SOS04,Kol05] with the strength of robust statistics. To reach this new definition, we review MLS surfaces in terms of local kernel regression, opening the doors to a vast and well established literature from which we utilize robust kernel regression. Our novel representation can handle sparse sampling, generates a continuous surface better preserving fine details, and can naturally handle any kind of sharp features with controllable sharpness. Finally, it combines ease of implementation with performance competing with other non-robust approaches.
dc.language.isoen
dc.publisherWiley
dc.subject.enPoint Based Graphics
dc.subject.enMoving Least Squares
dc.subject.enKernel Regression
dc.subject.enRobust Statistics
dc.title.enFeature Preserving Point Set Surfaces based on Non-Linear Kernel Regression
dc.typeArticle de revue
dc.subject.halInformatique [cs]/Synthèse d'image et réalité virtuelle [cs.GR]
bordeaux.journalComputer Graphics Forum
bordeaux.page493--501
bordeaux.volume28
bordeaux.hal.laboratoriesLaboratoire Bordelais de Recherche en Informatique (LaBRI) - UMR 5800*
bordeaux.issue2
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierinria-00354969
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//inria-00354969v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Computer%20Graphics%20Forum&rft.date=2009&rft.volume=28&rft.issue=2&rft.spage=493--501&rft.epage=493--501&rft.eissn=0167-7055&rft.issn=0167-7055&rft.au=OZTIRELI,%20Cengiz&GUENNEBAUD,%20Ga%C3%ABl&GROSS,%20Markus&rft.genre=article


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