MaskingDepth: Masked Consistency Regularization for Semi-supervised Monocular Depth Estimation

• URL: http://arxiv.org/abs/2212.10806v3
• Date: Thu, 23 Mar 2023 23:05:20 GMT
• Title: MaskingDepth: Masked Consistency Regularization for Semi-supervised Monocular Depth Estimation
• Authors: Jongbeom Baek, Gyeongnyeon Kim, Seonghoon Park, Honggyu An, Matteo Poggi, Seungryong Kim
• Abstract summary: MaskingDepth is a novel semi-supervised learning framework for monocular depth estimation. It enforces consistency between the strongly-augmented unlabeled data and the pseudo-labels derived from weakly-augmented unlabeled data.
• Score: 38.09399326203952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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