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Indexed by:期刊论文

Date of Publication:2014-04-15

Journal:Journal of Computational Information Systems

Included Journals:EI、Scopus

Volume:10

Issue:8

Page Number:3503-3510

ISSN No.:15539105

Abstract:The detection of feature lines plays an important role in representing and understanding geometric features of 3D models. In this paper, a new method for extracting feature lines from unorganized noisy point clouds is presented. First, feature points closing to the potential feature lines are detected by using a multi-scale operator, namely, the difference of normals. Undesired points may be possibly regarded as feature points due to the noisy point clouds and can then be easily eliminated by simply checking the feature points density. Finally, the detected feature points are divided into small groups and each group is represented by a landmark. These landmarks consist of vertices of poly-lines representing feature lines. Experimental results show that the proposed method inherits the robustness of the multi-scale operator and can be used to deal with the unorganized noisy point clouds even with data missing. ? 2014 Binary Information Press.