  In this paper, we explore the possibility of achieving a more accurate depth
estimation by fusing monocular images and Radar points using a deep neural
network. We give a comprehensive study of the fusion between RGB images and
Radar measurements from different aspects and proposed a working solution based
on the observations. We find that the noise existing in Radar measurements is
one of the main key reasons that prevents one from applying the existing fusion
methods developed for LiDAR data and images to the new fusion problem between
Radar data and images. The experiments are conducted on the nuScenes dataset,
which is one of the first datasets which features Camera, Radar, and LiDAR
recordings in diverse scenes and weather conditions. Extensive experiments
demonstrate that our method outperforms existing fusion methods. We also
provide detailed ablation studies to show the effectiveness of each component
in our method.
