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

Cengiz Oztireli (1) , Gaël Guennebaud (2, 3, 4) , Markus Gross (1)

1 ETH Zürich - Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich]
2 CNR | ISTI - CNR Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo” [Pisa]
3 IPARLA - Visualization and manipulation of complex data on wireless mobile devices
4 LaBRI - Laboratoire Bordelais de Recherche en Informatique

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.

Mots clés: Point Based Graphics; Moving Least Squares; Kernel Regression; Robust Statistics

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]

Cengiz Oztireli, Gaël Guennebaud, Markus Gross. Feature Preserving Point Set Surfaces based on Non-Linear Kernel Regression. Computer Graphics Forum, 2009, Proceedings of Eurographics 2009, 28 (2), pp.493--501. ⟨inria-00354969⟩