This paper aims to propose a new optimal hierarchical clustering approach to 3D mobile light detection and ranging (LiDAR) point clouds. The hierarchical clustering is performed on unorganized point clouds based on a proximity matrix that consists of a distance term and a direction term. In the dissimilarity calculation of two clusters, a pair of points from each of two clusters is selected, respectively, and Euclidean distances between the points are employed to define the distance term. The direction term is obtained by the differences of normal vectors at chosen points. The main contribution is that the cluster combination in the hierarchical clustering is optimized by a point-based graph model. The cluster combination is formulated as a problem of matching, optimized by finding the minimum-cost perfect matching in a bipartite graph. The results show that the proposed hierarchical clustering method succeeds in segmenting object from point clouds without any human–computer interaction and outperforms the state-of-the-art segmentation approaches in terms of completeness and correctness.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    An Optimal Hierarchical Clustering Approach to Mobile LiDAR Point Clouds


    Contributors:
    Xu, Sheng (author) / Wang, Ruisheng (author) / Wang, Hao (author) / Zheng, Han (author)


    Publication date :

    2020-07-01


    Size :

    28108562 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Detection of Cars in Mobile Lidar Point Clouds

    Li, Guorui / Fang, Xinwei / Khoshelham, Kourosh et al. | IEEE | 2018



    GCN-Based Pavement Crack Detection Using Mobile LiDAR Point Clouds

    Feng, Huifang / Li, Wen / Luo, Zhipeng et al. | IEEE | 2022


    Three-Dimensional Object Co-Localization From Mobile LiDAR Point Clouds

    Guo, Wenzhong / Chen, Jiawei / Wang, Weipeng et al. | IEEE | 2021


    Rapid Extraction of Urban Road Guardrails From Mobile LiDAR Point Clouds

    Gao, Jianlan / Chen, Yiping / Junior, Jose Marcato et al. | IEEE | 2022