  We tackle the problem of unsupervised synthetic-to-real domain adaptation for
single image depth estimation. An essential building block of single image
depth estimation is an encoder-decoder task network that takes RGB images as
input and produces depth maps as output. In this paper, we propose a novel
training strategy to force the task network to learn domain invariant
representations in a selfsupervised manner. Specifically, we extend
self-supervised learning from traditional representation learning, which works
on images from a single domain, to domain invariant representation learning,
which works on images from two different domains by utilizing an image-to-image
translation network. Firstly, we use an image-to-image translation network to
transfer domain-specific styles between synthetic and real domains. This style
transfer operation allows us to obtain similar images from the different
domains. Secondly, we jointly train our task network and Siamese network with
the same images from the different domains to obtain domain invariance for the
task network. Finally, we fine-tune the task network using labeled synthetic
and unlabeled realworld data. Our training strategy yields improved
generalization capability in the real-world domain. We carry out an extensive
evaluation on two popular datasets for depth estimation, KITTI and Make3D. The
results demonstrate that our proposed method outperforms the state-of-the-art
on all metrics, e.g. by 14.7% on Sq Rel on KITTI. The source code and model
weights will be made available.
