  We propose MaskingDepth, a novel semi-supervised learning framework for
monocular depth estimation to mitigate the reliance on large ground-truth depth
quantities. MaskingDepth is designed to enforce consistency between the
strongly-augmented unlabeled data and the pseudo-labels derived from
weakly-augmented unlabeled data, which enables learning depth without
supervision. In this framework, a novel data augmentation is proposed to take
the advantage of a naive masking strategy as an augmentation, while avoiding
its scale ambiguity problem between depths from weakly- and strongly-augmented
branches and risk of missing small-scale instances. To only retain
high-confident depth predictions from the weakly-augmented branch as
pseudo-labels, we also present an uncertainty estimation technique, which is
used to define robust consistency regularization. Experiments on KITTI and
NYU-Depth-v2 datasets demonstrate the effectiveness of each component, its
robustness to the use of fewer depth-annotated images, and superior performance
compared to other state-of-the-art semi-supervised methods for monocular depth
estimation. Furthermore, we show our method can be easily extended to domain
adaptation task. Our code is available at
https://github.com/KU-CVLAB/MaskingDepth.
