prs-eth / marigold-depth-hr-v1-1

High-Resolution Marigold Depth v1-0 Model Card

This is a model card for the marigold-depth-hr-v1-0 model for monocular depth estimation from a single image. The model is fine-tuned from the marigold-depth-v1-0 model as described in our papers:

• CVPR'2024 paper titled "Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation"
• Journal extension titled "Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis"

Model Details

• Developed by: Bingxin Ke , Kevin Qu , Tianfu Wang , Nando Metzger , Shengyu Huang , Bo Li , Anton Obukhov , Konrad Schindler .
• Model type: Generative latent diffusion-based affine-invariant monocular depth estimation from a single image.
• Language: English.
• License: Apache License License Version 2.0 .
• Model Description: This model can be used to generate an estimated depth map of an input image.
  • Resolution : The model is designed to support large resolutions up to 4MP.
  • Steps and scheduler : This model was designed for usage with the DDIM scheduler and between 10 and 50 denoising steps.
  • Outputs :
    • Affine-invariant depth map : The predicted values are between 0 and 1, interpolating between the near and far planes of the model's choice.
• Resources for more information: Project Website , Paper , Code .
• Cite as:

@misc{ke2025marigold,
  title={Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis},
  author={Bingxin Ke and Kevin Qu and Tianfu Wang and Nando Metzger and Shengyu Huang and Bo Li and Anton Obukhov and Konrad Schindler},
  year={2025},
  eprint={2505.09358},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@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}
}