The new generation of 4D high-resolution imaging radar provides not only a huge amount of point cloud but also additional elevation measurement, which has a great potential of 3D sensing in autonomous driving. In this paper, we introduce an autonomous driving dataset named TJ4DRadSet, including multi-modal sensors that are 4D radar, lidar, camera and GNSS, with about 40K frames in total. 7757 frames within 44 consecutive sequences in various driving scenarios are well annotated with 3D bounding boxes and track id. We provide a 4D radar-based 3D object detection baseline for our dataset to demonstrate the effectiveness of deep learning methods for 4D radar point clouds.
翻译：新一代的4D高分辨率成像雷达不仅提供了大量的点云,而且还提供了额外的海拔测量,这在自主驾驶中具有3D感测的巨大潜力。在本文中,我们引入了一个名为TJ4DRADSet的自动驱动数据集,包括4D雷达、里雷达、照相机和全球导航卫星系统的多式传感器,总共约40K框架。 各种驾驶情景中44个连续序列的7757个框架配有3D捆绑框和轨道ID,并配有良好的说明。我们为我们的数据集提供了一个基于4D的3D雷达物体探测基线,以展示4D雷达点云的深学习方法的有效性。
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