Feature Preserving Point Set Surfaces based on Non-Linear Kernel Regression
| hal.structure.identifier | Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] [ETH Zürich] | |
| dc.contributor.author | OZTIRELI, Cengiz | |
| hal.structure.identifier | CNR Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo” [Pisa] [CNR | ISTI] | |
| hal.structure.identifier | Visualization and manipulation of complex data on wireless mobile devices [IPARLA ] | |
| hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
| dc.contributor.author | GUENNEBAUD, Gaël | |
| hal.structure.identifier | Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] [ETH Zürich] | |
| dc.contributor.author | GROSS, Markus | |
| dc.date.accessioned | 2024-04-15T09:52:32Z | |
| dc.date.available | 2024-04-15T09:52:32Z | |
| dc.date.issued | 2009 | |
| dc.identifier.issn | 0167-7055 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/198525 | |
| dc.description.abstractEn | Moving 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.iso | en | |
| dc.publisher | Wiley | |
| dc.subject.en | Point Based Graphics | |
| dc.subject.en | Moving Least Squares | |
| dc.subject.en | Kernel Regression | |
| dc.subject.en | Robust Statistics | |
| dc.title.en | Feature Preserving Point Set Surfaces based on Non-Linear Kernel Regression | |
| dc.type | Article de revue | |
| dc.subject.hal | Informatique [cs]/Synthèse d'image et réalité virtuelle [cs.GR] | |
| bordeaux.journal | Computer Graphics Forum | |
| bordeaux.page | 493--501 | |
| bordeaux.volume | 28 | |
| bordeaux.hal.laboratories | Laboratoire Bordelais de Recherche en Informatique (LaBRI) - UMR 5800 | * |
| bordeaux.issue | 2 | |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | inria-00354969 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//inria-00354969v1 | |
| bordeaux.COinS | ctx_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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