title: DepthMaster: Taming Diffusion Models for Monocular Depth Estimation
summary: Monocular depth estimation within the diffusion-denoising paradigm
  demonstrates impressive generalization ability but suffers from low inference
  speed. Recent methods adopt a single-step deterministic paradigm to improve
  inference efficiency while maintaining comparable performance. However, they
  overlook the gap between generative and discriminative features, leading to
  suboptimal results. In this work, we propose DepthMaster, a single-step
  diffusion model designed to adapt generative features for the discriminative
  depth estimation task. First, to mitigate overfitting to texture details
  introduced by generative features, we propose a Feature Alignment module, which
  incorporates high-quality semantic features to enhance the denoising network's
  representation capability. Second, to address the lack of fine-grained details
  in the single-step deterministic framework, we propose a Fourier Enhancement
  module to adaptively balance low-frequency structure and high-frequency
  details. We adopt a two-stage training strategy to fully leverage the potential
  of the two modules. In the first stage, we focus on learning the global scene
  structure with the Feature Alignment module, while in the second stage, we
  exploit the Fourier Enhancement module to improve the visual quality. Through
  these efforts, our model achieves state-of-the-art performance in terms of
  generalization and detail preservation, outperforming other diffusion-based
  methods across various datasets. Our project page can be found at
  https://indu1ge.github.io/DepthMaster_page.
paper_authors._id: 677d15386ddea8749283f9c1
paper_authors.name: Ziyang Song
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paper_authors.fullname: Ziyang Song
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paper_authors.name: Zerong Wang
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paper_authors.name: Bo Li
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paper_authors.fullname: Bo Li
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paper_authors.name: Hao Zhang
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paper_authors.fullname: ZhangHao
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paper_authors.user: PierreHaoZHANG
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paper_authors.name: Ruijie Zhu
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paper_authors.fullname: Ruijie Zhu
paper_authors.type: user
paper_authors.user: RuijieZhu
paper_authors._id: 677d15386ddea8749283f9c6
paper_authors.name: Li Liu
paper_authors._id: 677d15386ddea8749283f9c7
paper_authors.name: Peng-Tao Jiang
paper_authors.status: admin_assigned
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paper_authors._id: 6423efbdb77cc3daf8429755
paper_authors.avatarUrl: /avatars/a5b480713b4dd1dae8191545cb4c6f94.svg
paper_authors.fullname: Peng-Tao Jiang
paper_authors.type: user
paper_authors.user: ptjiang
paper_authors._id: 677d15386ddea8749283f9c8
paper_authors.name: Tianzhu Zhang
paper_authors.status: admin_assigned
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paper_authors.fullname: Tianzhu Zhang
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paper_authors.user: ztz1989
paper_title: DepthMaster: Taming Diffusion Models for Monocular Depth Estimation
paper_summary: Monocular depth estimation within the diffusion-denoising paradigm
  demonstrates impressive generalization ability but suffers from low inference
  speed. Recent methods adopt a single-step deterministic paradigm to improve
  inference efficiency while maintaining comparable performance. However, they
  overlook the gap between generative and discriminative features, leading to
  suboptimal results. In this work, we propose DepthMaster, a single-step
  diffusion model designed to adapt generative features for the discriminative
  depth estimation task. First, to mitigate overfitting to texture details
  introduced by generative features, we propose a Feature Alignment module, which
  incorporates high-quality semantic features to enhance the denoising network's
  representation capability. Second, to address the lack of fine-grained details
  in the single-step deterministic framework, we propose a Fourier Enhancement
  module to adaptively balance low-frequency structure and high-frequency
  details. We adopt a two-stage training strategy to fully leverage the potential
  of the two modules. In the first stage, we focus on learning the global scene
  structure with the Feature Alignment module, while in the second stage, we
  exploit the Fourier Enhancement module to improve the visual quality. Through
  these efforts, our model achieves state-of-the-art performance in terms of
  generalization and detail preservation, outperforming other diffusion-based
  methods across various datasets. Our project page can be found at
  https://indu1ge.github.io/DepthMaster_page.
paper_ai_keywords: diffusion-denoising paradigm monocular depth estimation single-step deterministic paradigm generative features discriminative features overfitting texture details Feature Alignment module semantic features denoising network Fourier Enhancement module low-frequency structure high-frequency details two-stage training strategy global scene structure visual quality state-of-the-art performance generalization detail preservation