Radar Velocity Transformer: Single-scan Moving Object Segmentation in Noisy Radar Point Clouds
Journal:
arXiv
Published Date:
Jul 4, 2025
Abstract
The awareness about moving objects in the surroundings of a self-driving
vehicle is essential for safe and reliable autonomous navigation. The
interpretation of LiDAR and camera data achieves exceptional results but
typically requires to accumulate and process temporal sequences of data in
order to extract motion information. In contrast, radar sensors, which are
already installed in most recent vehicles, can overcome this limitation as they
directly provide the Doppler velocity of the detections and, hence incorporate
instantaneous motion information within a single measurement. % In this paper,
we tackle the problem of moving object segmentation in noisy radar point
clouds. We also consider differentiating parked from moving cars, to enhance
scene understanding. Instead of exploiting temporal dependencies to identify
moving objects, we develop a novel transformer-based approach to perform
single-scan moving object segmentation in sparse radar scans accurately. The
key to our Radar Velocity Transformer is to incorporate the valuable velocity
information throughout each module of the network, thereby enabling the precise
segmentation of moving and non-moving objects. Additionally, we propose a
transformer-based upsampling, which enhances the performance by adaptively
combining information and overcoming the limitation of interpolation of sparse
point clouds. Finally, we create a new radar moving object segmentation
benchmark based on the RadarScenes dataset and compare our approach to other
state-of-the-art methods. Our network runs faster than the frame rate of the
sensor and shows superior segmentation results using only single-scan radar
data.
