Reproducing and Extending RaDelft 4D Radar with Camera-Assisted Labels

TL;DR

This paper reproduces the RaDelft 4D radar dataset results and introduces a camera-assisted labeling method that improves radar data annotation accuracy, enabling better research in adverse conditions.

Contribution

It presents a reproducible framework for generating accurate radar labels using camera guidance, addressing dataset limitations and enhancing radar semantic segmentation research.

Findings

Camera-guided labeling improves radar annotation accuracy

Reproducibility of RaDelft results achieved

Fog levels negatively impact radar labeling performance

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.