  Even though many existing 3D object detection algorithms rely mostly on
camera and LiDAR, camera and LiDAR are prone to be affected by harsh weather
and lighting conditions. On the other hand, radar is resistant to such
conditions. However, research has found only recently to apply deep neural
networks on radar data. In this paper, we introduce a deep learning approach to
3D object detection with radar only. To the best of our knowledge, we are the
first ones to demonstrate a deep learning-based 3D object detection model with
radar only that was trained on the public radar dataset. To overcome the lack
of radar labeled data, we propose a novel way of making use of abundant LiDAR
data by transforming it into radar-like point cloud data and aggressive radar
augmentation techniques.
