  Scene understanding plays an essential role in enabling autonomous driving
and maintaining high standards of performance and safety. To address this task,
cameras and laser scanners (LiDARs) have been the most commonly used sensors,
with radars being less popular. Despite that, radars remain low-cost,
information-dense, and fast-sensing techniques that are resistant to adverse
weather conditions. While multiple works have been previously presented for
radar-based scene semantic segmentation, the nature of the radar data still
poses a challenge due to the inherent noise and sparsity, as well as the
disproportionate foreground and background. In this work, we propose a novel
approach to the semantic segmentation of radar scenes using a multi-input
fusion of radar data through a novel architecture and loss functions that are
tailored to tackle the drawbacks of radar perception. Our novel architecture
includes an efficient attention block that adaptively captures important
feature information. Our method, TransRadar, outperforms state-of-the-art
methods on the CARRADA and RADIal datasets while having smaller model sizes.
https://github.com/YahiDar/TransRadar

Computer Vision and Pattern Recognition;Artificial Intelligence