Application Research of Ripley’s K-function in Point Cloud Data Shape Analysis

  • Linlin TangEmail author
  • Xupeng Tong
  • Jingyong Su
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1107)


Classification and recognition of objects for images is an important issue in many scientific fields such as computer vision, biometrics and medical image analysis. An important feature of many objects is shape, so shape analysis has become an important part of classification. One method of shape analysis is to estimate boundaries and analyze the shape of these boundaries to determining the characteristics of the original object. However, many literature studies on point cloud shape analysis are based on existing shapes. This paper mainly refers to Ripley’s K-function in spatial point analysis, through this judgment on spatial distribution of point cloud data to determine the existence of shape in point cloud data, through the spatial distribution of 2D point cloud data and 3D point cloud data. Judging by the randomness of the experiment, K-function has a considerable effect on judging existence of point cloud data shape through relevant experimental verification analysis.


Point cloud data Ripley’s K-function Spatial randomness 



This work was supported by Shenzhen Science and Technology Plan Fundamental Research Funding JCYJ20180306171938767 and Shenzhen Foundational Research Funding JCYJ20180507183527919.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Harbin Institute of TechnologyShenzhenChina

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