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README.md

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-license: apache-2.0
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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". -->
+
+<!-- [![Website](doc/badges/badge-website.svg)](https://marigoldmonodepth.github.io)
+[![Paper](https://img.shields.io/badge/arXiv-PDF-b31b1b)](https://arxiv.org/abs/2312.02145) -->
+
+<!-- [![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0) -->
+
+<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> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
+<!-- <a href='https://arxiv.org/abs/[]'><img src='https://img.shields.io/badge/arXiv-[]-b31b1b.svg'></a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -->
+ <a href='https://indu1ge.github.io/DepthMaster_page/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
+ <a href='https://github.com/indu1ge/DepthMaster'><img src='https://img.shields.io/badge/GitHub-Repository-blue?logo=github'></a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
+ <a href='https://www.apache.org/licenses/LICENSE-2.0'><img src='https://img.shields.io/badge/License-Apache--2.0-929292'></a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
+ <!-- <a href='https://paperswithcode.com/sota/unsupervised-monocular-depth-estimation-on-7?p=ec-depth-exploring-the-consistency-of-self'><img src='https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/ec-depth-exploring-the-consistency-of-self/unsupervised-monocular-depth-estimation-on-7'></a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -->
+</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. -->
+
+
+![teaser](assets/framework.png)
+
+<!-- >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).
+
+[![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0)