The 4D millimeter-wave (mmWave) radar, with its robustness in extreme environments, extensive detection range, and capabilities for measuring velocity and elevation, has demonstrated significant potential for enhancing the perception abilities of autonomous driving systems in corner-case scenarios, such as rainy, snowy or foggy weathers. Nevertheless, the inherent sparsity and noise of 4D mmWave radar point clouds restrict its further deployment and practical application. In this paper, we introduce a novel 4D mmWave radar point cloud detector, which leverages high-resolution dense LiDAR point clouds. Our approach constructs dense 3D occupancy ground truth from stitched LiDAR point clouds, and employs a specially designed network named DenserRadar. The proposed method surpasses existing probability-based and learning-based radar point cloud detectors in terms of both point cloud density and accuracy on the K-Radar dataset.


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    Titel :

    DenserRadar: A 4D Millimeter-Wave Radar Point Cloud Detector Based on Dense LiDAR Point Clouds


    Beteiligte:
    Han, Zeyu (Autor:in) / Jiang, Junkai (Autor:in) / Ding, Xiaokang (Autor:in) / Wang, Jiahao (Autor:in) / Meng, Qingwen (Autor:in) / Xu, Shaobing (Autor:in) / He, Lei (Autor:in) / Wang, Jianqiang (Autor:in)


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    1817943 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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