Update README.md

README.md

@@ -36,14 +36,14 @@ The model is fine-tuned from the `marigold-e2e-ft` [model](https://huggingface.c
 described in our paper:
 - [Paper](paper link) titled "Panorama Geometry Estimation using Single-Step Diffusion Models"
 
-
-You can also check out our [scale-invariant depth estimation model](https://huggingface.co/prs-eth/PaGeR-depth), [metric depth estimation model](https://huggingface.co/prs-eth/PaGeR-metric-depth), or test models in our [demo](https://huggingface.co/spaces/prs-eth/PaGeR).
+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 monocular panoramic surface normal estimation from a single ERP image.
 - **License:** [CreativeML OpenRAIL License](LICENSE).
 - **Model Description:** This model can be used to generate an estimated normals 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**:
     - **Surface Normals map**: The predicted values represent a 3D normals map in range 0-1, representing the 3D surface normal vector at each pixel.