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An Automatic Density Clustering Segmentation Method for Laser Scanning Point Cloud Data of Buildings

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  • Jianghong Zhao
  • Yan Dong
  • Siyu Ma
  • Huajun Liu
  • Shuangfeng Wei
  • Ruiju Zhang
  • Xi Chen

Abstract

Segmentation is an important step in point cloud data feature extraction and three-dimensional modelling. Currently, it is also a challenging problem in point cloud processing. There are some disadvantages of the DBSCAN method, such as requiring the manual definition of parameters and low efficiency when it is used for large amounts of calculation. This paper proposes the AQ-DBSCAN algorithm, which is a density clustering segmentation method combined with Gaussian mapping. The algorithm improves upon the DBSCAN algorithm by solving the problem of automatic estimation of the parameter neighborhood radius. The improved algorithm can carry out density clustering processing quickly by reducing the amount of computation required.

Suggested Citation

  • Jianghong Zhao & Yan Dong & Siyu Ma & Huajun Liu & Shuangfeng Wei & Ruiju Zhang & Xi Chen, 2019. "An Automatic Density Clustering Segmentation Method for Laser Scanning Point Cloud Data of Buildings," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-13, July.
    • Handle: RePEc:hin:jnlmpe:3026758
    DOI: 10.1155/2019/3026758
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