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README.md
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---
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license: apache-2.0
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language:
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- en
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pipeline_tag: depth-estimation
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tags:
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- monocular depth estimation
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- single image depth estimation
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- depth
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- in-the-wild
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- zero-shot
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- depth
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---
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# Marigold: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
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This model represents the official LCM checkpoint of the paper titled "Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation".
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[](https://marigoldmonodepth.github.io)
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[](https://github.com/prs-eth/Marigold)
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[](https://arxiv.org/abs/2312.02145)
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[](https://colab.research.google.com/drive/12G8reD13DdpMie5ZQlaFNo2WCGeNUH-u?usp=sharing)
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[](https://huggingface.co/spaces/toshas/marigold)
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[](https://www.apache.org/licenses/LICENSE-2.0)
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<!-- []() -->
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<!-- []() -->
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<!-- []() -->
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<!-- ### [Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation]() -->
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[Bingxin Ke](http://www.kebingxin.com/),
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[Anton Obukhov](https://www.obukhov.ai/),
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[Shengyu Huang](https://shengyuh.github.io/),
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[Nando Metzger](https://nandometzger.github.io/),
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[Rodrigo Caye Daudt](https://rcdaudt.github.io/),
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[Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ&hl=en )
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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.
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## 🎓 Citation
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```bibtex
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@InProceedings{ke2023repurposing,
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title={Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation},
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author={Bingxin Ke and Anton Obukhov and Shengyu Huang and Nando Metzger and Rodrigo Caye Daudt and Konrad Schindler},
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booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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year={2024}
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}
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```
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## 🎫 License
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This work is licensed under the Apache License, Version 2.0 (as defined in the [LICENSE](LICENSE.txt)).
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By downloading and using the code and model you agree to the terms in the [LICENSE](LICENSE.txt).
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[](https://www.apache.org/licenses/LICENSE-2.0)
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