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:
@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.
🎫 License
This work is licensed under the Apache License, Version 2.0 (as defined in the LICENSE).
By downloading and using the code and model you agree to the terms in the LICENSsE.
