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<!-- # DepthMaster: Taming Diffusion Models for Monocular Depth Estimation
This repository represents the official implementation of the paper titled "DepthMaster: Taming Diffusion Models for Monocular Depth Estimation". -->
<!-- [](https://marigoldmonodepth.github.io)
[](https://arxiv.org/abs/2312.02145) -->
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<h1 align="center"><strong>DepthMaster: Taming Diffusion Models for Monocular Depth Estimation</strong></h1>
<p align="center">
<a href="https://indu1ge.github.io/ziyangsong">Ziyang Song*</a>,
<a href="https://orcid.org/0009-0001-6677-0572">Zerong Wang*</a>,
<a href="https://orcid.org/0000-0001-7817-0665">Bo Li</a>,
<a href="https://orcid.org/0009-0007-1175-5918">Hao Zhang</a>,
<a href="https://ruijiezhu94.github.io/ruijiezhu/">Ruijie Zhu</a>,
<a href="https://orcid.org/0009-0004-3280-8490">Li Liu</a>,
<a href="https://pengtaojiang.github.io/">Peng-Tao Jiang†</a>,
<a href="http://staff.ustc.edu.cn/~tzzhang/">Tianzhu Zhang†</a>,
<br>
*Equal Contribution, †Corresponding Author
<br>
University of Science and Technology of China, vivo Mobile Communication Co., Ltd.
<br>
<b>Arxiv 2025</b>
</p>
<!-- [Ziyang Song*](https://indu1ge.github.io/ziyangsong),
[Zerong Wang*](),
[Bo Li](https://orcid.org/0000-0001-7817-0665),
[Hao Zhang](https://orcid.org/0009-0007-1175-5918),
[Ruijie Zhu](https://ruijiezhu94.github.io/ruijiezhu/),
[Li Liu](https://orcid.org/0009-0004-3280-8490)
[Tianzhu Zhang](http://staff.ustc.edu.cn/~tzzhang/)
[Peng-Tao Jiang](https://pengtaojiang.github.io/) -->
<div align="center">
<a href='https://arxiv.org/abs/2501.02576'><img src='https://img.shields.io/badge/Paper-arXiv-red'></a>
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</div>
<!-- We present Marigold, a diffusion model, and associated fine-tuning protocol for monocular depth estimation. Its core principle is to leverage the rich visual knowledge stored in modern generative image models. Our model, derived from Stable Diffusion and fine-tuned with synthetic data, can zero-shot transfer to unseen data, offering state-of-the-art monocular depth estimation results. -->
<!-- >We present DepthMaster, a tamed single-step diffusion model designed to enhance the generalization and detail preservation abilities of depth estimation models. Through feature alignment, we effectively prevent the overfitting to texture details. By adaptively enhance -->
>We present DepthMaster, a tamed single-step diffusion model that customizes generative features in diffusion models to suit the discriminative depth estimation task. We introduce a Feature Alignment module to mitigate overfitting to texture and a Fourier Enhancement module to refine fine-grained details. DepthMaster exhibits state-of-the-art zero-shot performance and superior detail preservation ability, surpassing
other diffusion-based methods across various datasets.
## 🎓 Citation
Please cite our paper:
```bibtex
@article{song2025depthmaster,
title={DepthMaster: Taming Diffusion Models for Monocular Depth Estimation},
author={Song, Ziyang and Wang, Zerong and Li, Bo and Zhang, Hao and Zhu, Ruijie and Liu, Li and Jiang, Peng-Tao and Zhang, Tianzhu},
journal={arXiv preprint arXiv:2501.02576},
year={2025}
}
```
## Acknowledgements
The code is based on [Marigold](https://github.com/prs-eth/Marigold).
## 🎫 License
This work is licensed under the Apache License, Version 2.0 (as defined in the [LICENSE](LICENSE.txt)).
By downloading and using the code and model you agree to the terms in the [LICENSsE](LICENSE.txt).
[](https://www.apache.org/licenses/LICENSE-2.0) |