  In this paper, we address the problem of monocular depth estimation when only
a limited number of training image-depth pairs are available. To achieve a high
regression accuracy, the state-of-the-art estimation methods rely on CNNs
trained with a large number of image-depth pairs, which are prohibitively
costly or even infeasible to acquire. Aiming to break the curse of such
expensive data collections, we propose a semi-supervised adversarial learning
framework that only utilizes a small number of image-depth pairs in conjunction
with a large number of easily-available monocular images to achieve high
performance. In particular, we use one generator to regress the depth and two
discriminators to evaluate the predicted depth , i.e., one inspects the
image-depth pair while the other inspects the depth channel alone. These two
discriminators provide their feedbacks to the generator as the loss to generate
more realistic and accurate depth predictions. Experiments show that the
proposed approach can (1) improve most state-of-the-art models on the NYUD v2
dataset by effectively leveraging additional unlabeled data sources; (2) reach
state-of-the-art accuracy when the training set is small, e.g., on the Make3D
dataset; (3) adapt well to an unseen new dataset (Make3D in our case) after
training on an annotated dataset (KITTI in our case).
