DepthMaster: Taming Diffusion Models for Monocular Depth Estimation
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.
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Ziyang Song
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2025-01-07T13:23:41.821Z
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Ziyang Song
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Zerong Wang
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Zerong.Wang
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Bo Li
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2025-01-07T13:22:55.281Z
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Bo Li
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Hao Zhang
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2025-01-13T08:54:43.137Z
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ZhangHao
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PierreHaoZHANG
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Ruijie Zhu
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2025-01-07T13:22:26.716Z
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Ruijie Zhu
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RuijieZhu
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Li Liu
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Peng-Tao Jiang
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2025-01-07T13:22:04.674Z
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Peng-Tao Jiang
user
ptjiang
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Tianzhu Zhang
admin_assigned
2025-01-07T13:21:57.347Z
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Tianzhu Zhang
user
ztz1989
DepthMaster: Taming Diffusion Models for Monocular Depth Estimation
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.
DepthMaster, a single-step diffusion model with Feature Alignment and Fourier Enhancement modules, achieves state-of-the-art performance in monocular depth estimation by balancing generative and discriminative features.
diffusion-denoising paradigm monocular depth estimation deterministic paradigm generative features discriminative features DepthMaster Feature Alignment module Fourier Enhancement module two-stage training strategy global scene structure visual quality