用摄像头辅助生成雷达语义标签，解决数据稀缺难题
Reproducing and Extending RaDelft 4D Radar with Camera-Assisted Labels
- 通过摄像头分割结果投影+空间聚类生成雷达标签
- 在不同雾天条件下验证标签准确性，量化影响程度
- 开源可复现框架，推动4D雷达研究发展
近期4D雷达在恶劣环境感知中展现潜力，但语义分割进展受限于公开数据集和标注的缺乏。RaDelft数据集虽具开创性，仅提供激光雷达标注且无公开代码生成雷达标签，制约复现与后续研究。本文复现了RaDelft组的数值结果，证明基于摄像头引导的雷达标注流程可在无需人工标注的情况下生成精准雷达点云标签。通过将雷达点云投影至摄像头语义分割结果并应用空间聚类，显著提升标签精度。该工作建立可复现框架，使研究社区能够训练与评估标注后的4D雷达数据。此外，我们系统研究并量化了不同雾度对雷达标注性能的影响。
原文摘要 · Abstract (English)
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.
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