Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 210800017-6.doi: 10.11896/jsjkx.210800017

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Point Cloud Feature Line Extraction Algorithm Based on PCPNET

YU Meng-juan, NIE Jian-hui   

  1. School of Automation,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:YU Meng-juan,born in 1997,postgra-duate.Her main research interests include discrete geometric processing and so on.
    NIE Jian-hui,born in 1984,Ph.D,associate professor,postgraduate supervisor,is a member of China Computer Federation.His main research interests include geometric processing and optical measurement.
  • Supported by:
    National Natural Science Foundation of China(61802240).

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Abstract: Feature line extraction is the basic operation of geometric model processing,which is of great significance to the expression of 3D model.Based on PCPNET,a calculation method of curvature value and principal curvature direction which is robust to noise and non-uniform sampling is proposed,and a feature line extraction algorithm is proposed.The proposed algorithm uses the weighted quadratic curve to fit the local curvature distribution,and realizes the recognition of ridge and valley feature points by determining the distance from the extreme point of the quadratic curve in the direction of maximum principal curvature.Finally,the minimum spanning tree(MST) of the refined potential feature points is established to connect the feature points and complete the feature line extraction.Experimental results show that the proposed algorithm can use PCPNET to accurately estimate the curvature and principal curvature direction information of point cloud,and according to the proposed feature point recognition method,it can overcome the defect that the traditional simple threshold truncation can not extract the feature lines of flat area normally,and finally extract the feature lines from clean point cloud and noise point cloud accurately and completely.

Key words: Point cloud, Characteristic line, PCPNET, Curvature

CLC Number: 

  • TP391
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