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A Nonparametric Approach for Noisy Point Data Preprocessing  

Xi, Yongjian (University of Missouri)
Duan, Ye (University of Missouri)
Zhao, Hongkai (University of California at Irvine)
Publication Information
International Journal of CAD/CAM / v.9, no.1, 2010 , pp. 31-36 More about this Journal
Abstract
3D point data acquired from laser scan or stereo vision can be quite noisy. A preprocessing step is often needed before a surface reconstruction algorithm can be applied. In this paper, we propose a nonparametric approach for noisy point data preprocessing. In particular, we proposed an anisotropic kernel based nonparametric density estimation method for outlier removal, and a hill-climbing line search approach for projecting data points onto the real surface boundary. Our approach is simple, robust and efficient. We demonstrate our method on both real and synthetic point datasets.
Keywords
Point Preprocessing; Outlier Removal; Surface Reconstruction; Parzen Window; Anisotropic Kernel Density Estimation;
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