Abstract
Recent advances in 4D radar highlight its potential for robust environment perception under adverse conditions, yet progress in radar semantic segmentation remains constrained by the scarcity of open source datasets and labels. The RaDelft data set, although seminal, provides only LiDAR annotations and no public code to generate radar labels, limiting reproducibility and downstream research. In this work, we reproduce the numerical results of the RaDelft group and demonstrate that a camera-guided radar labeling pipeline can generate accurate labels for radar point clouds without relying on human annotations. By projecting radar point clouds into camera-based semantic segmentation and applying spatial clustering, we create labels that significantly enhance the accuracy of radar labels. These results establish a reproducible framework that allows the research community to train and evaluate the labeled 4D radar data. In addition, we study and quantify how different fog levels affect the radar labeling performance.
Abstract (translated)
最近在4D雷达领域的进展突显了其在恶劣条件下进行稳健环境感知的潜力，然而，由于开源数据集和标签的稀缺性，雷达语义分割的进步受到了限制。虽然RaDelft数据集具有开创性意义，但它仅提供了LiDAR标注，并且没有公开用于生成雷达标签的代码，这限制了研究的可重复性和后续研究的发展。 在本工作中，我们重现了RaDelft团队的数值结果，并展示了通过相机引导的雷达标记流水线可以在不依赖人工注释的情况下为雷达点云生成准确的标签。通过将雷达点云投影到基于相机的语义分割中并应用空间聚类方法，我们可以创建出能够显著提高雷达标签准确性的标注。这些成果建立了一个可重复的研究框架，允许研究社区对标记后的4D雷达数据进行训练和评估。 此外，我们还探讨并量化了不同雾气水平如何影响雷达标记性能。
URL
https://arxiv.org/abs/2512.02394
