论文标题
TJ4DRADSET：用于自动驾驶的4D雷达数据集
TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving
论文作者
论文摘要
下一代高分辨率汽车雷达（4D雷达）可以提供额外的高程测量和较密集的点云，这具有在自动驾驶中3D传感的巨大潜力。在本文中，我们介绍了一个名为TJ4Dradset的数据集，其中有4D雷达点用于自动驾驶研究。该数据集是在各种驾驶场景中收集的，连续44个序列中总共有7757个同步框架，并用3D边界框和轨道ID进行了很好的注释。我们为数据集提供了基于4D雷达的3D对象检测基线，以证明深度学习方法对4D雷达点云的有效性。可以通过以下链接访问数据集：https：//github.com/tjradarlab/tj4dradset。
The next-generation high-resolution automotive radar (4D radar) can provide additional elevation measurement and denser point clouds, which has great potential for 3D sensing in autonomous driving. In this paper, we introduce a dataset named TJ4DRadSet with 4D radar points for autonomous driving research. The dataset was collected in various driving scenarios, with a total of 7757 synchronized frames in 44 consecutive sequences, which are well annotated with 3D bounding boxes and track ids. 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. The dataset can be accessed via the following link: https://github.com/TJRadarLab/TJ4DRadSet.
