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

dc.contributor.authorOeztireli, A. C.en_US
dc.contributor.authorGuennebaud, G.en_US
dc.contributor.authorGross, M.en_US
dc.date.accessioned2015-02-23T10:16:16Z
dc.date.available2015-02-23T10:16:16Z
dc.date.issued2009en_US
dc.description.abstractMoving 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.en_US
dc.description.number2en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume28en_US
dc.identifier.doi10.1111/j.1467-8659.2009.01388.xen_US
dc.identifier.issn1467-8659en_US
dc.identifier.pages493-501en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2009.01388.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltden_US
dc.titleFeature Preserving Point Set Surfaces based on Non-Linear Kernel Regressionen_US
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