Recently, 3D deep learning technologies require a large amount of supervised 3D point-cloud data to learn statistical models for various ITS-related tasks, e.g. object classification, object detection, object segmentation, etc. However, manually annotating 3D point-cloud data is time-consuming and labor-intensive. Therefore, this paper aims at co-locating 3D objects from mobile LiDAR point clouds without any help of supervised training data. To realize it, we propose a new framework to implement 3D object co-localization for automatically extracting the objects of the same category from different point-cloud scenes. Specifically, to search and exploit the co-information from objects in different point-cloud scenes, we formulate a 3D object co-localization problem as a maximal subgraph matching problem. During the graph construction procedure, to handle the inconsistent representation of objects in different scenes, we propose a multi-scale clustering method to represent objects by a pyramid structure. In addition, because the maximal subgraph matching problem is NP-hard, we propose a stochastic search algorithm to generate the co-localization results. Extensive experiments on the point-cloud data collected by the Reigl VMX450 mobile LiDAR system demonstrate the promising performance of the proposed framework.


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    Title :

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


    Contributors:
    Guo, Wenzhong (author) / Chen, Jiawei (author) / Wang, Weipeng (author) / Luo, Huan (author) / Wang, Shiping (author)


    Publication date :

    2021-04-01


    Size :

    3254825 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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