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    Point Cloud Instance Segmentation using Probabilistic Embeddings

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    Thumbnail
    Name:
    Preprintfile1.pdf
    Size:
    28.80Mb
    Format:
    PDF
    Description:
    Pre-print
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    Thumbnail
    Name:
    Preprintfile1.pdf
    Size:
    28.80Mb
    Format:
    PDF
    Description:
    Pre-print
    Download
    Type
    Preprint
    Authors
    Zhang, Biao
    Wonka, Peter cc
    KAUST Department
    King Abdullah University for Science and Technology
    Computer Science Program
    Visual Computing Center (VCC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-11-30
    Permanent link to this record
    http://hdl.handle.net/10754/660736
    
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    Abstract
    In this paper we propose a new framework for point cloud instance segmentation. Our framework has two steps: an embedding step and a clustering step. In the embedding step, our main contribution is to propose a probabilistic embedding space for point cloud embedding. Specifically, each point is represented as a tri-variate normal distribution. In the clustering step, we propose a novel loss function, which benefits both the semantic segmentation and the clustering. Our experimental results show important improvements to the SOTA, i.e., 3.1% increased average per-category mAP on the PartNet dataset.
    Publisher
    arXiv
    arXiv
    1912.00145
    Additional Links
    https://arxiv.org/pdf/1912.00145
    Collections
    Preprints; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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