  Accurate real depth annotations are difficult to acquire, needing the use of
special devices such as a LiDAR sensor. Self-supervised methods try to overcome
this problem by processing video or stereo sequences, which may not always be
available. Instead, in this paper, we propose a domain adaptation approach to
train a monocular depth estimation model using a fully-annotated source dataset
and a non-annotated target dataset. We bridge the domain gap by leveraging
semantic predictions and low-level edge features to provide guidance for the
target domain. We enforce consistency between the main model and a second model
trained with semantic segmentation and edge maps, and introduce priors in the
form of instance heights. Our approach is evaluated on standard domain
adaptation benchmarks for monocular depth estimation and show consistent
improvement upon the state-of-the-art.
