Semantic labeling of road scenes using colorized mobile LiDAR point clouds is of great significance in a variety of applications, particularly intelligent transportation systems. However, many challenges, such as incompleteness of objects caused by occlusion, overlapping between neighboring objects, interclass local similarities, and computational burden brought by a huge number of points, make it an ongoing open research area. In this paper, we propose a novel patch-based framework for labeling road scenes of colorized mobile LiDAR point clouds. In the proposed framework, first, three-dimensional (3-D) patches extracted from point clouds are used to construct a 3-D patch-based match graph structure (3D-PMG), which transfers category labels from labeled to unlabeled point cloud road scenes efficiently. Then, to rectify the transferring errors caused by local patch similarities in different categories, contextual information among 3-D patches is exploited by combining 3D-PMG with Markov random fields. In the experiments, the proposed framework is validated on colorized mobile LiDAR point clouds acquired by the RIEGL VMX-450 mobile LiDAR system. Comparative experiments show the superior performance of the proposed framework for accurate semantic labeling of road scenes.


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    Title :

    Patch-Based Semantic Labeling of Road Scene Using Colorized Mobile LiDAR Point Clouds


    Contributors:
    Luo, Huan (author) / Wang, Cheng (author) / Wen, Chenglu (author) / Cai, Zhipeng (author) / Chen, Ziyi (author) / Wang, Hanyun (author) / Yu, Yongtao (author) / Li, Jonathan (author)


    Publication date :

    2016-05-01


    Size :

    2455349 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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