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About this paper
Botao Sun, Ignacio Roldan, Francesco Fioranelli
A novel automatic labelling process is introduced to generate point-by-point multi-class ground truth for 4D radar tensors by fusing LiDAR and camera data. This enables training a semantic segmentation network that processes 4D radar directly, achieving a 13.2% improvement in vehicle detection probability and a 0.54m reduction in Chamfer distance compared to adapted literature variants.
Key insights
- Automatic multi-sensor data fusion from LiDAR and cameras provides a scalable method for generating high-quality point-by-point ground truth labels for complex 4D radar tensors.
- Direct semantic segmentation on 4D Range-Azimuth-Elevation (RAE) radar tensors is feasible and achieves detailed environmental understanding, particularly for close-by and dynamic objects.
- Grouping similar, challenging classes like pedestrians and bicycles into a single "Vulnerable Road User" (VRU) category can enhance overall semantic segmentation performance on radar data.
Summary by alphaXiv.
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