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

Oeztireli, A. C.; Guennebaud, G.; Gross, M.

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

Date

2009

Authors

Oeztireli, A. C.
Guennebaud, G.
Gross, M.

Publisher

The Eurographics Association and Blackwell Publishing Ltd

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.

        @article{10.1111:j.1467-8659.2009.01388.x
,
journal = {Computer Graphics Forum},
title = {{Feature Preserving Point Set Surfaces based on Non-Linear Kernel Regression
}},
author = {Oeztireli, A. C. and 
Guennebaud, G. and 
Gross, M.
},
year = {2009
},
publisher = {The Eurographics Association and Blackwell Publishing Ltd
},
ISSN = {1467-8659
},
DOI = {10.1111/j.1467-8659.2009.01388.x
}
}

URI

https://doi.org/10.1111/j.1467-8659.2009.01388.x

Collections

28-Issue 2
Full Papers 2009 - CGF 28-Issue 2

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