  A distinctive feature of Doppler radar is the measurement of velocity in the
radial direction for radar points. However, the missing tangential velocity
component hampers object velocity estimation as well as temporal integration of
radar sweeps in dynamic scenes. Recognizing that fusing camera with radar
provides complementary information to radar, in this paper we present a
closed-form solution for the point-wise, full-velocity estimate of Doppler
returns using the corresponding optical flow from camera images. Additionally,
we address the association problem between radar returns and camera images with
a neural network that is trained to estimate radar-camera correspondences.
Experimental results on the nuScenes dataset verify the validity of the method
and show significant improvements over the state-of-the-art in velocity
estimation and accumulation of radar points.
