OZTIRELI, Cengiz; GUENNEBAUD, Gaël; GROSS, Markus

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OZTIRELI, Cengiz
Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] [ETH Zürich]

GUENNEBAUD, Gaël
CNR Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo” [Pisa] [CNR | ISTI]
Visualization and manipulation of complex data on wireless mobile devices [IPARLA ]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]

GROSS, Markus
Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] [ETH Zürich]

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Computer Graphics Forum. 2009, vol. 28, n° 2, p. 493--501

Wiley

Résumé en anglais

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.< Réduire

Mots clés en anglais

Point Based Graphics

Moving Least Squares

Kernel Regression

Robust Statistics

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

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