Radar Instance Transformer: Reliable Moving Instance Segmentation in
  Sparse Radar Point Clouds
Matthias Zeller Vardeep S. Sandhu Benedikt Mersch Jens Behley Michael Heidingsfeld Cyrill Stachniss
The perception of moving objects is crucial for autonomous robots performing
collision avoidance in dynamic environments. LiDARs and cameras tremendously
enhance scene interpretation but do not provide direct motion information and
face limitations under adverse weather. Radar sensors overcome these
limitations and provide Doppler velocities, delivering direct information on
dynamic objects. In this paper, we address the problem of moving instance
segmentation in radar point clouds to enhance scene interpretation for
safety-critical tasks. Our Radar Instance Transformer enriches the current
radar scan with temporal information without passing aggregated scans through a
neural network. We propose a full-resolution backbone to prevent information
loss in sparse point cloud processing. Our instance transformer head
incorporates essential information to enhance segmentation but also enables
reliable, class-agnostic instance assignments. In sum, our approach shows
superior performance on the new moving instance segmentation benchmarks,
including diverse environments, and provides model-agnostic modules to enhance
scene interpretation. The benchmark is based on the RadarScenes dataset and
will be made available upon acceptance.