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Jongbeom Baek

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2 papers
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2

IROS Conference 2024 Conference Paper

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

  • Jongbeom Baek
  • Gyeongnyeon Kim
  • Seonghoon Park 0002
  • Honggyu An
  • Matteo Poggi
  • Seungryong Kim

We propose MaskingDepth, a semi-supervised learning framework for monocular depth estimation. MaskingDepth is designed to enforce consistency between the depths obtained from strongly-augmented images and the pseudo-depths derived from weakly-augmented images, which enables mitigating the reliance on large ground-truth depth quantities. In this framework, we leverage uncertainty estimation to only retain high-confident depth predictions from the weakly-augmented branch as pseudo-depths. We also present a novel data augmentation, dubbed K-way disjoint masking, that takes advantage of a naïve token 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 objects. 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 learning methods for monocular depth estimation.

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ICRA Conference 2022 Conference Paper

Semi-Supervised Learning with Mutual Distillation for Monocular Depth Estimation

  • Jongbeom Baek
  • Gyeongnyeon Kim
  • Seungryong Kim

We propose a semi-supervised learning framework for monocular depth estimation. Compared to existing semi-supervised learning methods, which inherit limitations of both sparse supervised and unsupervised loss functions, we achieve the complementary advantages of both loss functions, by building two separate network branches for each loss and distilling each other through the mutual distillation loss function. We also present to apply different data augmentation to each branch, which improves the robustness. We conduct experiments to demonstrate the effectiveness of our framework over the latest methods and provide extensive ablation studies.

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