Due to the difficulty of obtaining ground-truth labels, learning from virtual-world datasets is of great interest for real-world applications like semantic segmentation.
In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision.
Ranked #4 on Monocular Depth Estimation on KITTI Eigen split
On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals.
Depth estimation provides essential information to perform autonomous driving and driver assistance.
Ranked #19 on Monocular Depth Estimation on KITTI Eigen split