Summary

Proceedings of the 2012 International Symposium on Nonlinear Theory and its Applications

2012

Session Number:A2L-A

Session:

Number:82

Scalable Curvature-based Feature Detection for Unorganized 3D Point Sets

Yu Guo,  Fei Wang,  Xuan Wang,  Bei Tian,  Xuetao Zhang,  

pp.82-85

Publication Date:

Online ISSN:2188-5079

DOI:10.15248/proc.1.82

PDF download (1.7MB)

The information of unorganized 3D point cloud data is limited because of no explicit topologic structure and relations between points. So it is difficult to extract the feature from the 3D point cloud data directly. In this paper, a framework of extracting local salient features from 3D point cloud is presented. We use the geometry properties to detect the features of the 3D point clouds and propose a curvature based multi-scale feature point detection method for unorganized point clouds. Our proposed multi-scale representation for 3D unorganized point cloud is defined in terms of an estimation of point cloud surface curvatures according to local neighborhoods with different sizes. Points corresponding to local extrema of curvature are selected as feature points. We also proposed a quality measure to rank the feature points and select the best ones.Experimental results show that this new approach can detect features effectively and robustly for 3D point cloud data.

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