Abstract
In monocular depth estimation, unsupervised domain adaptation has recently
been explored to relax the dependence on large annotated image-based depth
datasets. However, this comes at the cost of training multiple models or
requiring complex training protocols. We formulate unsupervised domain
adaptation for monocular depth estimation as a consistency-based
semi-supervised learning problem by assuming access only to the source domain
ground truth labels. To this end, we introduce a pairwise loss function that
regularises predictions on the source domain while enforcing perturbation
consistency across multiple augmented views of the unlabelled target samples.
Importantly, our approach is simple and effective, requiring only training of a
single model in contrast to the prior work. In our experiments, we rely on the
standard depth estimation benchmarks KITTI and NYUv2 to demonstrate
state-of-the-art results compared to related approaches. Furthermore, we
analyse the simplicity and effectiveness of our approach in a series of
ablation studies. The code is available at
\url{https://github.com/AmirMaEl/SemiSupMDE}.