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    Leveraging Shape Completion for 3D Siamese Tracking

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    Type
    Conference Paper
    Authors
    Giancola, Silvio
    Zarzar, Jesus
    Ghanem, Bernard cc
    KAUST Department
    Visual Computing Center (VCC)
    KAUST
    Electrical Engineering Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-01-10
    Preprint Posting Date
    2019-03-05
    Online Publication Date
    2020-01-10
    Print Publication Date
    2019-06
    Permanent link to this record
    http://hdl.handle.net/10754/660667
    
    Metadata
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    Abstract
    Point clouds are challenging to process due to their sparsity, therefore autonomous vehicles rely more on appearance attributes than pure geometric features. However, 3D LIDAR perception can provide crucial information for urban navigation in challenging light or weather conditions. In this paper, we investigate the versatility of Shape Completion for 3D Object Tracking in LIDAR point clouds. We design a Siamese tracker that encodes model and candidate shapes into a compact latent representation. We regularize the encoding by enforcing the latent representation to decode into an object model shape. We observe that 3D object tracking and 3D shape completion complement each other. Learning a more meaningful latent representation shows better discriminatory capabilities, leading to improved tracking performance. We test our method on the KITTI Tracking set using car 3D bounding boxes. Our model reaches a 76.94% Success rate and 81.38% Precision for 3D Object Tracking, with the shape completion regularization leading to an improvement of 3% in both metrics.
    Citation
    Giancola, S., Zarzar, J., & Ghanem, B. (2019). Leveraging Shape Completion for 3D Siamese Tracking. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2019.00145
    Sponsors
    This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. RGC/3/3570-01-01.
    Publisher
    IEEE
    Conference/Event name
    2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    DOI
    10.1109/CVPR.2019.00145
    arXiv
    1903.01784
    Additional Links
    https://ieeexplore.ieee.org/document/8954383/
    https://ieeexplore.ieee.org/document/8954383/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8954383
    ae974a485f413a2113503eed53cd6c53
    10.1109/CVPR.2019.00145
    Scopus Count
    Collections
    Preprints; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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