The accelerating development of autonomous driving technology has placed greater demands on obtaining large amounts of high-quality data. Representative, labeled, real world data serves as the fuel for training deep learning networks, critical for improving self-driving perception algorithms. In this paper, we introduce PandaSet, the first dataset produced by a complete, high-precision autonomous vehicle sensor kit with a no-cost commercial license. The dataset was collected using one 360° mechanical spinning LiDAR, one forward-facing, long-range LiDAR, and 6 cameras. The dataset contains more than 100 scenes, each of which is 8 seconds long, and provides 28 types of labels for object classification and 37 types of labels for semantic segmentation. We provide baselines for LiDAR-only 3D object detection, LiDAR-camera fusion 3D object detection and LiDAR point cloud segmentation. For more details about PandaSet and the development kit, see https://scale.com/open-datasets/pandaset.


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    Titel :

    PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving


    Beteiligte:
    Xiao, Pengchuan (Autor:in) / Shao, Zhenlei (Autor:in) / Hao, Steven (Autor:in) / Zhang, Zishuo (Autor:in) / Chai, Xiaolin (Autor:in) / Jiao, Judy (Autor:in) / Li, Zesong (Autor:in) / Wu, Jian (Autor:in) / Sun, Kai (Autor:in) / Jiang, Kun (Autor:in)


    Erscheinungsdatum :

    19.09.2021


    Format / Umfang :

    2487309 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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