In this paper, a method by applying deep learning method onto the point clouds data for semantic segmentation is proposed. Three convolutional neural networks, PointNet, PointNet++, and DGCNN, are replicated, designed, and analyzed. In order to avoid problems introduced by some other methods due to the preprocessing step, here, PointNet, PointNet++, and DGCNN are directly used onto the 3D point cloud. Experiments verified the effect of these neural networks on point clouds for semantic segmentation. Methods based on PointNet and PointNet++ show good results, while DGCNN-based reached state-of-the-art performance.


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    Titel :

    Deep Learning on 3D Point Cloud for Semantic Segmentation


    Weitere Titelangaben:

    Smart Innovation, Systems and Technologies


    Beteiligte:
    Wu, Tsu-Yang (Herausgeber:in) / Ni, Shaoquan (Herausgeber:in) / Chu, Shu-Chuan (Herausgeber:in) / Chen, Chi-Hua (Herausgeber:in) / Favorskaya, Margarita (Herausgeber:in) / Ning, Zhihan (Autor:in) / Tang, Linlin (Autor:in) / Qi, Shuhan (Autor:in) / Liu, Yang (Autor:in)


    Erscheinungsdatum :

    30.11.2021


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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