用激光雷达与摄像头自动标注雷达数据，实现高精度语义分割。
Automatic Labelling & Semantic Segmentation with 4D Radar Tensors
- 融合激光雷达与摄像头信息自动标注雷达数据
- 雷达语义分割性能达激光雷达的65%，车辆检测率提升13.2%
- 适合自动驾驶中多传感器融合与雷达感知研究者
本文提出一种面向汽车数据集的自动标注方法，利用激光雷达与摄像头的互补信息生成标签。这些标签作为真值，结合对应4D雷达数据输入到所提出的语义分割网络中，为每个空间体素分配类别标签。在公开的RaDelft数据集上验证了该方法的有效性，所提网络性能达到激光雷达检测性能的65%以上，车辆检测概率提升13.2%，且相比文献中基线模型的Chamfer距离减少0.54米。
原文摘要 · Abstract (English)
In this paper, an automatic labelling process is presented for automotive datasets, leveraging on complementary information from LiDAR and camera. The generated labels are then used as ground truth with the corresponding 4D radar data as inputs to a proposed semantic segmentation network, to associate a class label to each spatial voxel. Promising results are shown by applying both approaches to the publicly shared RaDelft dataset, with the proposed network achieving over 65% of the LiDAR detection performance, improving 13.2% in vehicle detection probability, and reducing 0.54 m in terms of Chamfer distance, compared to variants inspired from the literature.
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