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
https://github.com/AmirMaEl/SemiSupMDE.

在单目深度估计中，我们提出了一种基于一致性的半监督学习方法来解决无监督领域自适应问题，通过在源领域的预测结果和未标记目标样本的多个增强视图上的扰动一致性来实现源领域的预测规则化。与先前的方法相比，我们的方法简单且有效，只需要训练单个模型，实验结果表明，在 KITTI 和 NYUv2 等深度估计基准上与相关方法相比具有最先进的性能。