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

机译：建筑物激光扫描点云数据的自动密度聚类分割方法

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摘要

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.
机译：分段是点云数据特征提取和三维建模的重要步骤。目前，在点云处理中也是一个具有挑战性的问题。 DBSCAN方法存在一些缺点，例如在使用大量计算时需要手动定义和低效率。本文提出了AQ-DBSCAN算法，其是一种与高斯映射结合的密度聚类分割方法。该算法通过解决参数邻域半径的​​自动估计问题来改善DBSCAN算法。改进的算法可以通过减少所需的计算量来快速进行密度聚类处理。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第15期|21.1-21.13|共13页
  • 作者

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

  • 作者单位

    Beijing Univ Civil Engn & Architecture Sch Geoinformat & Urban Informat Beijing Peoples R China;

    Beijing Univ Civil Engn & Architecture Sch Geoinformat & Urban Informat Beijing Peoples R China;

    Beijing Univ Civil Engn & Architecture Sch Geoinformat & Urban Informat Beijing Peoples R China;

    Huanqiujingwei Surveying & Mapping Engn Beijing C Beijing Peoples R China;

    Beijing Univ Civil Engn & Architecture Sch Geoinformat & Urban Informat Beijing Peoples R China;

    Beijing Univ Civil Engn & Architecture Sch Geoinformat & Urban Informat Beijing Peoples R China;

    Beijing Univ Civil Engn & Architecture Beijing Res Base Architectural Culture Beijing Peoples R China;

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