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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    An Optimal Hierarchical Clustering Approach to Mobile LiDAR Point Clouds


    Beteiligte:
    Xu, Sheng (Autor:in) / Wang, Ruisheng (Autor:in) / Wang, Hao (Autor:in) / Zheng, Han (Autor:in)


    Erscheinungsdatum :

    01.07.2020


    Format / Umfang :

    28108562 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    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


    Extracting Highway Cross Slopes From Airborne and Mobile LiDAR Point Clouds

    Shams, Alireza / Sarasua, Wayne A. / Russell, Brook T. et al. | Transportation Research Record | 2022