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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    Title :

    Deep Learning on 3D Point Cloud for Semantic Segmentation


    Additional title:

    Smart Innovation, Systems and Technologies


    Contributors:
    Wu, Tsu-Yang (editor) / Ni, Shaoquan (editor) / Chu, Shu-Chuan (editor) / Chen, Chi-Hua (editor) / Favorskaya, Margarita (editor) / Ning, Zhihan (author) / Tang, Linlin (author) / Qi, Shuhan (author) / Liu, Yang (author)


    Publication date :

    2021-11-30


    Size :

    8 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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