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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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- LCM |
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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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[![Website](doc/badges/badge-website.svg)](https://marigoldmonodepth.github.io) |
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[![GitHub](https://img.shields.io/github/stars/prs-eth/Marigold?style=default&label=GitHub%20β
&logo=github)](https://github.com/prs-eth/Marigold) |
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[![Paper](doc/badges/badge-pdf.svg)](https://arxiv.org/abs/2312.02145) |
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[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/12G8reD13DdpMie5ZQlaFNo2WCGeNUH-u?usp=sharing) |
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[![Hugging Face Space](https://img.shields.io/badge/π€%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/toshas/marigold) |
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[![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0) |
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<!-- [![HF Space](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Space-blue)]() --> |
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<!-- [![Open In Colab](doc/badges/badge-colab.svg)]() --> |
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<!-- [![Docker](doc/badges/badge-docker.svg)]() --> |
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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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![teaser](doc/teaser_collage_transparant.png) |
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## π 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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## π« 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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[![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0) |
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