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

Cengiz Oztireli 1 Gaël Guennebaud 2, 3, 4, * Markus Gross 1
* Corresponding author
3 IPARLA - Visualization and manipulation of complex data on wireless mobile devices
CNRS - Centre National de la Recherche Scientifique : UMR5800, École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Inria Bordeaux - Sud-Ouest, Université Sciences et Technologies - Bordeaux 1
Abstract : 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.
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https://hal.inria.fr/inria-00354969
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Submitted on : Monday, May 23, 2011 - 4:32:04 PM
Last modification on : Thursday, February 11, 2021 - 2:58:35 PM
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Cengiz Oztireli, Gaël Guennebaud, Markus Gross. Feature Preserving Point Set Surfaces based on Non-Linear Kernel Regression. Computer Graphics Forum, Wiley, 2009, Proceedings of Eurographics 2009, 28 (2), pp.493--501. ⟨inria-00354969⟩

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