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README.md
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<a title="arXiv" href="https://arxiv.org/abs/2505.09358" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
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<img src="https://img.shields.io/badge/%F0%9F%93%84%20Read%20-Paper-AF3436" alt="arXiv">
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</a>
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<a title="License" href="LICENSE"
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target="_blank" rel="noopener noreferrer" style="display: inline-block;">
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<img src="https://img.shields.io/badge/License-CreativeML%20OpenRAIL-7C3AED" alt="License">
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@@ -34,7 +37,7 @@ The model is fine-tuned from the `marigold-e2e-ft` [model](https://huggingface.c
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described in our paper:
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- [Paper](paper link) titled "Panorama Geometry Estimation using Single-Step Diffusion Models"
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You can also check out our [metric depth estimation model](https://huggingface.co/prs-eth/PaGeR-metric-depth),
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## Model Details
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- **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).
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- **Model type:** Generative latent diffusion-based one-step scale-invariant monocular panoramic depth estimation from a single ERP image.
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<a title="arXiv" href="https://arxiv.org/abs/2505.09358" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
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<img src="https://img.shields.io/badge/%F0%9F%93%84%20Read%20-Paper-AF3436" alt="arXiv">
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</a>
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<a title="Hugging Face" href="https://huggingface.co/spaces/prs-eth/PaGeR" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
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<img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-FFD21E" alt="Hugging Face Spaces">
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</a>
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<a title="License" href="LICENSE"
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target="_blank" rel="noopener noreferrer" style="display: inline-block;">
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<img src="https://img.shields.io/badge/License-CreativeML%20OpenRAIL-7C3AED" alt="License">
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described in our paper:
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- [Paper](paper link) titled "Panorama Geometry Estimation using Single-Step Diffusion Models"
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You can also check out our [metric depth estimation model](https://huggingface.co/prs-eth/PaGeR-metric-depth), [surface normals estimation model](https://huggingface.co/prs-eth/PaGeR-normals), or test it in our [demo](https://huggingface.co/spaces/prs-eth/PaGeR).
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## Model Details
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- **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).
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- **Model type:** Generative latent diffusion-based one-step scale-invariant monocular panoramic depth estimation from a single ERP image.
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