---
license: creativeml-openrail-m
tags:
- depth estimation
- panoramic images
- high resolution
- image analysis
- computer vision
- in-the-wild
- zero-shot
pipeline_tag: depth-estimation
---

<h1 align="center"> 📟 PaGeR(Panoramic Geometry Estimation) Depth Estimation Model Card</h1>

<p align="center">
<a title="Github" href="https://github.com/prs-eth/PaGeR" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
    <img src="https://img.shields.io/github/stars/prs-eth/PaGeR?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="Github">
</a>
<a title="Website" href="https://marigoldcomputervision.github.io/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
    <img src="https://img.shields.io/badge/%E2%99%A5%20Project%20-Website-blue" alt="Website">
</a>
<a title="arXiv" href="https://arxiv.org/abs/2505.09358" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
    <img src="https://img.shields.io/badge/%F0%9F%93%84%20Read%20-Paper-AF3436" alt="arXiv">
</a>
<a title="Hugging Face" href="https://huggingface.co/spaces/prs-eth/PaGeR" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
    <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-FFD21E" alt="Hugging Face Spaces">
</a>
<a title="License" href="LICENSE"
   target="_blank" rel="noopener noreferrer" style="display: inline-block;">
  <img src="https://img.shields.io/badge/License-CreativeML%20OpenRAIL-7C3AED" alt="License">
</a>
</p>

This is a model card for the `PaGeR-depth` model for monocular depth estimation from a single panoramic [ERP](https://en.wikipedia.org/wiki/Equirectangular_projection) image. 
The model is fine-tuned from the `marigold-e2e-ft` [model](https://huggingface.co/GonzaloMG/marigold-e2e-ft-depth) on synthetic [PanoInfinigen](insert link) dataset, as
described in our paper:
- [Paper](paper link) titled "Panorama Geometry Estimation using Single-Step Diffusion Models"

You can also check out other depth and normals models in our [collection](https://huggingface.co/collections/prs-eth/pager), or test models in our [demo](https://huggingface.co/spaces/prs-eth/PaGeR).
## Model Details
- **Developed by:** [Vukasin Bozic](https://vulus98.github.io/), [Isidora Slavkovic](https://linkedin.com/in/isidora-slavkovic), [Dominik Narnhofer](https://scholar.google.com/citations?user=tFx8AhkAAAAJ&hl=en), [Nando Metzger](https://nandometzger.github.io/), [Denis Rozumny](https://rozumden.github.io/), [Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ), [Nikolai Kalischek](https://scholar.google.com/citations?user=XwzlnZoAAAAJ&hl=de).
- **Model type:** Generative latent diffusion-based one-step scale-invariant monocular panoramic depth estimation from a single ERP image.
- **License:** [CreativeML OpenRAIL License](LICENSE).
- **Model Description:** This model can be used to generate an estimated depth map of a panoramic input image. 
  - **Resolution**: The model is designed to support large resolutions up to 3K.
  - **Dataset**: [PanoInfinigen]()
  - **Steps and scheduler**: This model works in a swift, one-step regime.
  - **Outputs**:
    - **Scale-invariant depth map**: The predicted values represent a depth map, interpolating between the near and far planes of the model's choice.
- **Resources for more information:** [Project Website](insert link), [Paper](insert link), [Code](https://github.com/prs-eth/PaGeR).
- **Cite as:**

```bibtex
Add citation
```