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