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\iteman{io-port 05650508}
\itemau{\"Oztireli, A.C.; Guennebaud, G.; Gross, M.}
\itemti{Feature preserving point set surfaces based on non-linear kernel regression.}
\itemso{Comput. Graph. Forum 28, No. 2, 493-501 (2009).}
\itemab
Summary: 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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\itemli{doi:10.1111/j.1467-8659.2009.01388.x}
\end