# Automatic Labelling & Semantic Segmentation with 4D Radar Tensors

Source: [Automatic Labelling & Semantic Segmentation with 4D Radar Tensors](<https://www.sota2.com/research/paper/68fa76b01e209131cea6e7d9>)

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

Data updated: 2025-10-26T04:00:18.171Z

Authors: Botao Sun, Ignacio Roldan, Francesco Fioranelli

Published: 2025-01-20T09:18:41.000Z

## Abstract

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.

## 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/2501.11351>)

[PDF](<https://arxiv.org/pdf/2501.11351.pdf>)