Point cloud completion is a critical task for intelligent vehicles in the Intelligent Transportation Systems (ITS). Due to the disordered nature of point clouds, current point-based point completion methods utilize max pooling operations and Convolutional Neural Networks (CNNs) with a convolution kernel size of 1 to extract point features. However, a network with a kernel size of 1 has a small receptive field and can only extract one point feature at a time; as a result, such a network struggles to precisely extract highly abstracted features. To address this issue, we propose a point cloud completion network with larger receptive fields, termed PLRF-Net, which is composed of two parts: a reconstruction network and a completion network. The reconstruction network focuses on repairing the structures of partial points and includes an offset-attention embedding module, a rearrangement module, several convolution layers with varying kernel sizes, a pooling and repeating layer, and transposed convolution layers with different kernel sizes. Meanwhile, the completion network strives to improve the precision of the completed point cloud and consists of several convolution layers, along with a downsampling and upsampling module. Experimental results indicate that our approach achieves superior quantitative and qualitative outcomes on both the Multiview Partial Point Cloud (MVP) and KITTI datasets.


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    Title :

    Point Cloud Completion by Larger Receptive Fields for Intelligent Vehicles


    Contributors:
    Zhang, Yunqing (author) / Zhang, Xiaotong (author) / Liu, Kunhua (author) / Lv, Yisheng (author) / He, Wei (author) / Chen, Long (author)


    Publication date :

    2023-09-24


    Size :

    4885644 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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