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

LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery