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    • 39-Issue 7
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    A Graph-based One-Shot Learning Method for Point Cloud Recognition

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    Date
    2020
    Author
    Fan, Zhaoxin
    Liu, Hongyan
    He, Jun
    Sun, Qi
    Du, Xiaoyong
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    Abstract
    Point cloud based 3D vision tasks, such as 3D object recognition, are critical to many real world applications such as autonomous driving. Many point cloud processing models based on deep learning have been proposed by researchers recently. However, they are all large-sample dependent, which means that a large amount of manually labelled training data are needed to train the model, resulting in huge labor cost. In this paper, to tackle this problem, we propose a One-Shot learning model for Point Cloud Recognition, namely OS-PCR. Different from previous methods, our method formulates a new setting, where the model only needs to see one sample per class once for memorizing at inference time when new classes are needed to be recognized. To fulfill this task, we design three modules in the model: an Encoder Module, an Edge-conditioned Graph Convolutional Network Module, and a Query Module. To evaluate the performance of the proposed model, we build a one-shot learning benchmark dataset for 3D point cloud analysis. Then, comprehensive experiments are conducted on it to demonstrate the effectiveness of our proposed model.
    BibTeX
    @article {10.1111:cgf.14147,
    journal = {Computer Graphics Forum},
    title = {{A Graph-based One-Shot Learning Method for Point Cloud Recognition}},
    author = {Fan, Zhaoxin and Liu, Hongyan and He, Jun and Sun, Qi and Du, Xiaoyong},
    year = {2020},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.14147}
    }
    URI
    https://doi.org/10.1111/cgf.14147
    https://diglib.eg.org:443/handle/10.1111/cgf14147
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA