For monocular depth estimation, acquiring ground truths for real data is not
easy, and thus domain adaptation methods are commonly adopted using the
supervised synthetic data. However, this may still incur a large domain gap due
to the lack of supervision from the real data. In this paper, we develop a
domain adaptation framework via generating reliable pseudo ground truths of
depth from real data to provide direct supervisions. Specifically, we propose
two mechanisms for pseudo-labeling: 1) 2D-based pseudo-labels via measuring the
consistency of depth predictions when images are with the same content but
different styles; 2) 3D-aware pseudo-labels via a point cloud completion
network that learns to complete the depth values in the 3D space, thus
providing more structural information in a scene to refine and generate more
reliable pseudo-labels. In experiments, we show that our pseudo-labeling
methods improve depth estimation in various settings, including the usage of
stereo pairs during training. Furthermore, the proposed method performs
favorably against several state-of-the-art unsupervised domain adaptation
approaches in real-world datasets.

利用真实数据生成可靠的伪地面真实数据，提供直接监督的领域适应框架，通过提出 2D 伪标签和 3D 感知伪标签等机制，实现深度评估的提高，有效地解决领域适应问题。