DepthMaster: Taming Diffusion Models for Monocular Depth Estimation
Authors: name: Ziyang Song, name: Zerong Wang, name: Bo Li, name: Hao Zhang, name: Ruijie Zhu, name: Li Liu, name: Peng-Tao Jiang, name: Tianzhu Zhang

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.