  Robust perception is a vital component for ensuring safe autonomous and
assisted driving. Automotive radar (77 to 81 GHz), which offers
weather-resilient sensing, provides a complementary capability to the vision-
or LiDAR-based autonomous driving systems. Raw radio-frequency (RF) radar
tensors contain rich spatiotemporal semantics besides 3D location information.
The majority of previous methods take in 3D (Doppler-range-azimuth) RF radar
tensors, allowing prediction of an object's location, heading angle, and size
in bird's-eye-view (BEV). However, they lack the ability to at the same time
infer objects' size, orientation, and identity in the 3D space. To overcome
this limitation, we propose an efficient joint architecture called
CenterRadarNet, designed to facilitate high-resolution representation learning
from 4D (Doppler-range-azimuth-elevation) radar data for 3D object detection
and re-identification (re-ID) tasks. As a single-stage 3D object detector,
CenterRadarNet directly infers the BEV object distribution confidence maps,
corresponding 3D bounding box attributes, and appearance embedding for each
pixel. Moreover, we build an online tracker utilizing the learned appearance
embedding for re-ID. CenterRadarNet achieves the state-of-the-art result on the
K-Radar 3D object detection benchmark. In addition, we present the first 3D
object-tracking result using radar on the K-Radar dataset V2. In diverse
driving scenarios, CenterRadarNet shows consistent, robust performance,
emphasizing its wide applicability.
