  An accurate depth map of the environment is critical to the safe operation of
autonomous robots and vehicles. Currently, either light detection and ranging
(LIDAR) or stereo matching algorithms are used to acquire such depth
information. However, a high-resolution LIDAR is expensive and produces sparse
depth map at large range; stereo matching algorithms are able to generate
denser depth maps but are typically less accurate than LIDAR at long range.
This paper combines these approaches together to generate high-quality dense
depth maps. Unlike previous approaches that are trained using ground-truth
labels, the proposed model adopts a self-supervised training process.
Experiments show that the proposed method is able to generate high-quality
dense depth maps and performs robustly even with low-resolution inputs. This
shows the potential to reduce the cost by using LIDARs with lower resolution in
concert with stereo systems while maintaining high resolution.
