Implicit Surface Fitting by Regularized Regression and Compactly Supported Radial Basis Functions

Pan Rongjiang

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Implicit Surface Fitting by Regularized Regression and Compactly Supported Radial Basis Functions

Pan Rongjiang * #

School of Computer Science and Technology，Shandong University

*Correspondence author

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Funding: none

Opened online:27 November 2007

Accepted by: none

Citation: Pan Rongjiang .Implicit Surface Fitting by Regularized Regression and Compactly Supported Radial Basis Functions[OL]. [27 November 2007] http://en.paper.edu.cn/en_releasepaper/content/16581

We describe a direct way of making use of surface normal vectors at sample points in the problem of implicit surface fitting with compactly supported radial basis functions. The normal vectors are incorporated in a regularized regression problem that leads to a n by n positive definite linear system given n surface point/normal pairs. Compared with the widely used heuristic methods, our method avoids of introducing manufactured off-surface points and can fit much larger datasets effectively. We demonstrate its robust performance on several datasets.

Keywords:Implicit Surface Fitting, Radial Basis Function, Regularized Regressio

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