PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Oztireli C., Guennebaud G., Gross M. Feature preserving point set surfaces based on non-linear kernel regression. In: Computer Graphics Forum, vol. 28 (2) pp. 493 - 502. Eurographics Association and Wiley Blackwell, 2009.
 
 
Abstract
(English)
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 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.
Subject Computer graphics
Point set surfaces
I.3.5 Computational Geometry and Object Modeling
65D18 Computer graphics and computational geometry


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