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DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain

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arxiv 2410.14980 v2 pith:GAC5MZHY submitted 2024-10-19 cs.CV

Kun Wang , Zhiqiang Yan , Junkai Fan , Wanlu Zhu , Xiang Li , Jun Li , Jian Yang This is my paper · ORCID
classification cs.CV
keywords depthcomponentsdcdepthdomainestimationfrequencycosinediscrete
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In this paper, we introduce DCDepth, a novel framework for the long-standing monocular depth estimation task. Moving beyond conventional pixel-wise depth estimation in the spatial domain, our approach estimates the frequency coefficients of depth patches after transforming them into the discrete cosine domain. This unique formulation allows for the modeling of local depth correlations within each patch. Crucially, the frequency transformation segregates the depth information into various frequency components, with low-frequency components encapsulating the core scene structure and high-frequency components detailing the finer aspects. This decomposition forms the basis of our progressive strategy, which begins with the prediction of low-frequency components to establish a global scene context, followed by successive refinement of local details through the prediction of higher-frequency components. We conduct comprehensive experiments on NYU-Depth-V2, TOFDC, and KITTI datasets, and demonstrate the state-of-the-art performance of DCDepth. Code is available at https://github.com/w2kun/DCDepth.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World

    cs.CV 2025-05 conditional novelty 6.0 of 10

    BadDepth uses poisoned depth labels and physical-world image augmentation to make a triggered object vanish from monocular depth predictions.

  2. Region-aware Depth Scale Adaptation with Sparse Measurements

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A non-learning method segments an image and gives each region its own scale and shift, fitted to a few sparse depth points, to turn relative monocular depth predictions into metric depth more accurately than a single ...

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