  The advent of deep learning has brought an impressive advance to monocular
depth estimation, e.g., supervised monocular depth estimation has been
thoroughly investigated. However, the large amount of the RGB-to-depth dataset
may not be always available since collecting accurate depth ground truth
according to the RGB image is a time-consuming and expensive task. Although the
network can be trained on an alternative dataset to overcome the dataset scale
problem, the trained model is hard to generalize to the target domain due to
the domain discrepancy. Adversarial domain alignment has demonstrated its
efficacy to mitigate the domain shift on simple image classification tasks in
previous works. However, traditional approaches hardly handle the conditional
alignment as they solely consider the feature map of the network. In this
paper, we propose an adversarial training model that leverages semantic
information to narrow the domain gap. Based on the experiments conducted on the
datasets for the monocular depth estimation task including KITTI and
Cityscapes, the proposed compact model achieves state-of-the-art performance
comparable to complex latest models and shows favorable results on boundaries
and objects at far distances.
