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

Source: [Reproducing and Extending RaDelft 4D Radar with Camera-Assisted Labels](<https://www.sota2.com/research/paper/692fc402d6b0dd3342933252>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-04T05:02:45.405Z

Authors: Kejia Hu, Mohammed Alsakabi, John M. Dolan, Ozan K. Tonguz

Published: 2025-12-02T04:12:41.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2512.02394>)

[PDF](<https://arxiv.org/pdf/2512.02394.pdf>)