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Surface Reconstruction with Enriched Reproducing Kernel Particle Approximation

hal.structure.identifierVisualization and manipulation of complex data on wireless mobile devices [IPARLA]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorREUTER, Patrick
hal.structure.identifierESTIA - Institute of technology [ESTIA]
dc.contributor.authorJOYOT, Pierre
hal.structure.identifierESTIA - Institute of technology [ESTIA]
dc.contributor.authorTRUNZLER, Jean
hal.structure.identifierVisualization and manipulation of complex data on wireless mobile devices [IPARLA]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorBOUBEKEUR, Tamy
hal.structure.identifierVisualization and manipulation of complex data on wireless mobile devices [IPARLA]
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorSCHLICK, Christophe
dc.date.accessioned2024-04-15T09:55:57Z
dc.date.available2024-04-15T09:55:57Z
dc.date.issued2005
dc.date.conference2005-07-01
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/198791
dc.description.abstractEnThere are many techniques that reconstruct continuous 3D surfaces from scattered point data coming from laser range scanners. One of the most commonly used representations are Point Set Surfaces (PSS) defined as the set of stationary points of a Moving Least Squares (MLS) projection operator. One interesting property of the MLS projection is to automatically filter out high frequency noise, that is usually present in raw data due to scanning errors. Unfortunately, the MLS projection also smoothes out any high frequency feature, such as creases or corners, that may be present in the scanned geometry, and does not offer any possibility to distinguish between such feature and noise. The main contribution of this paper, is to present an alternative projection operator for surface reconstruction, based on the Enriched Reproducing Kernel Particle Approximation (ERKPA), which allows the reconstruction process to account for high frequency features, by letting the user explicitly tag the corresponding areas of the scanned geometry.
dc.language.isoen
dc.subject.enPoint-sampled surfaces
dc.subject.enpoint set surfaces
dc.subject.enpoint-based surfaces
dc.subject.enmeshless representation
dc.subject.ensharp features
dc.subject.enpoint-based graphics
dc.title.enSurface Reconstruction with Enriched Reproducing Kernel Particle Approximation
dc.typeCommunication dans un congrès
dc.subject.halInformatique [cs]/Synthèse d'image et réalité virtuelle [cs.GR]
bordeaux.hal.laboratoriesLaboratoire Bordelais de Recherche en Informatique (LaBRI) - UMR 5800*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleEUROGRAPHICS Symposium on Point-Based Graphics
bordeaux.countryUS
bordeaux.conference.cityNew York
bordeaux.peerReviewedoui
hal.identifierinria-00260890
hal.version1
hal.invitednon
hal.proceedingsoui
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//inria-00260890v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2005&rft.au=REUTER,%20Patrick&JOYOT,%20Pierre&TRUNZLER,%20Jean&BOUBEKEUR,%20Tamy&SCHLICK,%20Christophe&rft.genre=unknown


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