A Nonparametric Approach for Noisy Point Data Preprocessing

摘要：

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.

作者: Yongjian Xi Ye Duan Hongkai Zhao

作者单位: University of Missouri, Columbia, MO 65211, USA University of California at Irvine, Irvine, CA, 92710, USA

会议类型: 国际会议

会议名称: 11th IEEE International Conference on Computer-Aided Design and Computer Graphics(第11届IEEE国际计算机辅助设计与图形学学术会议 IEEE CAD/GRAPHICS 2009)

会议地点: 黄山

会议语种:英文

页码: 217-222

在线出版日期: 2009-08-19（万方平台首次上网日期，不代表论文的发表时间）