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    PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement

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    Type
    Preprint
    Authors
    Zarzar, Jesus
    Giancola, Silvio
    Ghanem, Bernard cc
    KAUST Department
    King Abdullah University of Science and Technology (KAUST), Saudi Arabia
    Visual Computing Center (VCC)
    Electrical Engineering Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-11-27
    Permanent link to this record
    http://hdl.handle.net/10754/660739
    
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    Abstract
    In autonomous driving pipelines, perception modules provide a visual understanding of the surrounding road scene. Among the perception tasks, vehicle detection is of paramount importance for a safe driving as it identifies the position of other agents sharing the road. In our work, we propose PointRGCN: a graph-based 3D object detection pipeline based on graph convolutional networks (GCNs) which operates exclusively on 3D LiDAR point clouds. To perform more accurate 3D object detection, we leverage a graph representation that performs proposal feature and context aggregation. We integrate residual GCNs in a two-stage 3D object detection pipeline, where 3D object proposals are refined using a novel graph representation. In particular, R-GCN is a residual GCN that classifies and regresses 3D proposals, and C-GCN is a contextual GCN that further refines proposals by sharing contextual information between multiple proposals. We integrate our refinement modules into a novel 3D detection pipeline, PointRGCN, and achieve state-of-the-art performance on the easy difficulty for the bird eye view detection task.
    Publisher
    arXiv
    arXiv
    1911.12236
    Additional Links
    https://arxiv.org/pdf/1911.12236
    Collections
    Preprints; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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