Learnable keypoint detectors and descriptors are beginning to outperform classical hand-crafted feature extraction methods.
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 #7 on Monocular Depth Estimation on KITTI Eigen split
We interpret this task as a classification problem by limiting the maximum number of lane candidates to eight.
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.
To the best of our knowledge, no efficient and robust CNN-based end-to-end approach can be found in the literature for this kind of problem.
Depth estimation provides essential information to perform autonomous driving and driver assistance.
Ranked #23 on Monocular Depth Estimation on KITTI Eigen split