Boundary Extraction of Planar Segments from Clouds of Unorganised Points

  • Zezhong Xu
  • Cheng Qian
  • Xianju Fei
  • Yanbing Zhuang
  • Shibo Xu
  • Reinhard KletteEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12046)


Planar segment detection in 3D point clouds is of importance for 3D registration, segmentation or analysis. General methods for planarity detection just detect a planar segment and label the 3D points; a boundary of the planar segment is typically not considered; spatial position and scope of coplanar 3D points are neglected. This paper proposes a method for detecting planar segments and extracting boundaries for such segments, all from clouds of unorganised points. Altogether, this aims at describing completely a set of 3D points: If a planar segment is detected, not only the plane’s normal and distance from the origin to the plane are detected, but also the planar segments boundary. By analysing Hough voting (from 3D space into a Hough space), we deduce a relationship between a planar segment’s boundary and voting cells. Cells that correspond to the planar segments boundary are located. Six linear functions are fitted to the voting cells, and four vertices are computed based on the coefficients of fitted functions. The bounding box of the planar segment is determined and used to represent the spatial position and scope of coplanar 3D points. The proposed method is tested on synthetic and real-world 3D point clouds. Experimental results demonstrate that the proposed method directly detects planar segment’s boundaries from clouds of unorganised points. No knowledge about local or global structure of point clouds is required for applying the proposed technique.


Planar segment detection 3D clouds of points Bounding box 



This work was supported by the National Natural Science Foundation of China (61602063), Jiangsu Collaborative Innovation Center for Cultural Creativity (XYN1705), the Natural Science Foundation of Jiangsu Higher Education Institute (18KJB520004), and Changzhou Science and Technology Project (CE20175023). Authors thank Amita Dhiman for providing 3D road surface data.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zezhong Xu
    • 1
  • Cheng Qian
    • 1
  • Xianju Fei
    • 1
  • Yanbing Zhuang
    • 1
  • Shibo Xu
    • 2
  • Reinhard Klette
    • 3
    Email author
  1. 1.Changzhou Institute of TechnologyChangzhouChina
  2. 2.Changsha High-tech Engineering SchoolChangshaChina
  3. 3.Auckland University of TechnologyAucklandNew Zealand

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