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    Self-Supervised Learning of Local Features in 3D Point Clouds

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    Name:
    Thabet_Self-Supervised_Learning_of_Local_Features_in_3D_Point_Clouds_CVPRW_2020_paper.pdf
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    679.8Kb
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    Description:
    CVF Open Access version
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    Type
    Conference Paper
    Authors
    Thabet, Ali Kassem cc
    Alwassel, Humam cc
    Ghanem, Bernard cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    VCC Analytics Research Group
    Visual Computing Center (VCC)
    Date
    2020-07-28
    Online Publication Date
    2020-07-28
    Print Publication Date
    2020-06
    Permanent link to this record
    http://hdl.handle.net/10754/664502
    
    Metadata
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    Abstract
    We present a self-supervised task on point clouds, in order to learn meaningful point-wise features that encode local structure around each point. Our self-supervised network, operates directly on unstructured/unordered point clouds. Using a multi-layer RNN, our architecture predicts the next point in a point sequence created by a popular and fast Space Filling Curve, the Morton-order curve. The final RNN state (coined Morton feature) is versatile and can be used in generic 3D tasks on point clouds. Our experiments show how our self-supervised task results in features that are useful for 3D segmentation tasks, and generalize well between datasets. We show how Morton features can be used to significantly improve performance (+3% for 2 popular algorithms) in semantic segmentation of point clouds on the challenging and large-scale S3DIS dataset. We also show how our self-supervised network pretrained on S3DIS transfers well to another large-scale dataset, vKITTI, leading to 11% improvement. Our code is publicly available.1
    Citation
    Thabet, A., Alwassel, H., & Ghanem, B. (2020). Self-Supervised Learning of Local Features in 3D Point Clouds. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). doi:10.1109/cvprw50498.2020.00477
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Conference/Event name
    2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
    ISBN
    978-1-7281-9361-8
    DOI
    10.1109/CVPRW50498.2020.00477
    Additional Links
    https://ieeexplore.ieee.org/document/9150706/
    https://ieeexplore.ieee.org/document/9150706/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9150706
    https://openaccess.thecvf.com/content_CVPRW_2020/papers/w54/Thabet_Self-Supervised_Learning_of_Local_Features_in_3D_Point_Clouds_CVPRW_2020_paper.pdf
    ae974a485f413a2113503eed53cd6c53
    10.1109/CVPRW50498.2020.00477
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Electrical and Computer Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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