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Computer Science > Computer Vision and Pattern Recognition

arXiv:2204.13483 (cs)
[Submitted on 28 Apr 2022 (v1), last revised 27 Jul 2022 (this version, v3)]

Title:TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving

Authors:Lianqing Zheng, Zhixiong Ma, Xichan Zhu, Bin Tan, Sen Li, Kai Long, Weiqi Sun, Sihan Chen, Lu Zhang, Mengyue Wan, Libo Huang, Jie Bai
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Abstract: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: this https URL.
Comments: 2022 IEEE International Intelligent Transportation Systems Conference (ITSC 2022)
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2204.13483 [cs.CV]
  (or arXiv:2204.13483v3 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2204.13483
Related DOI: https://doi.org/10.1109/ITSC55140.2022.9922539

Submission history

From: Lianqing Zheng [view email]
[v1] Thu, 28 Apr 2022 13:17:06 UTC (2,635 KB)
[v2] Sat, 30 Apr 2022 06:15:11 UTC (2,634 KB)
[v3] Wed, 27 Jul 2022 09:46:06 UTC (2,700 KB)
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