  This paper addresses the problem of dense depth predictions from sparse
distance sensor data and a single camera image on challenging weather
conditions. This work explores the significance of different sensor modalities
such as camera, Radar, and Lidar for estimating depth by applying Deep Learning
approaches. Although Lidar has higher depth-sensing abilities than Radar and
has been integrated with camera images in lots of previous works, depth
estimation using CNN's on the fusion of robust Radar distance data and camera
images has not been explored much. In this work, a deep regression network is
proposed utilizing a transfer learning approach consisting of an encoder where
a high performing pre-trained model has been used to initialize it for
extracting dense features and a decoder for upsampling and predicting desired
depth. The results are demonstrated on Nuscenes, KITTI, and a Synthetic dataset
which was created using the CARLA simulator. Also, top-view zoom-camera images
captured from the crane on a construction site are evaluated to estimate the
distance of the crane boom carrying heavy loads from the ground to show the
usability in safety-critical applications.
