---
license: cc-by-nc-4.0
pipeline_tag: depth-estimation
tags:
- surface-normals
- normal-estimation
- panoramic-images
- equirectangular
- high-resolution
- computer-vision
- in-the-wild
- zero-shot
---

<h1 align="center"> 📟 PaGeR-Normals 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://pager360.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/2605.26368" 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="HF Collection" href="https://huggingface.co/collections/prs-eth/pager-697241d06b3733a6f18e4d39" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
    <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Collection-FFD21E" alt="HF Collection">
</a>
<a title="License" href="LICENSE" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
  <img src="https://img.shields.io/badge/License-CC%20BY--NC%204.0-yellowgreen" alt="License">
</a>
</p>

`PaGeR-Normals` is the **normals-only** variant of PaGeR released with the paper:

- **Paper:** [Unified Panoramic Geometry Estimation via Multi-View Foundation Models](https://huggingface.co/papers/2605.26368) — [arXiv:2605.26368](https://arxiv.org/abs/2605.26368)

From a single equirectangular (ERP) panorama, it produces a **dense surface-normal map** (unit vectors in the panorama's world frame) in a single forward pass. If you want depth, metric scale, and sky masking from the same model, use the unified [`prs-eth/PaGeR`](https://huggingface.co/prs-eth/PaGeR) checkpoint instead.

You can also browse the rest of our [PaGeR HF collection](https://huggingface.co/collections/prs-eth/pager-697241d06b3733a6f18e4d39) or try the [interactive 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:** Feed-forward, multi-view foundation-model adaptation for single-image panoramic surface-normal estimation.
- **Backbone:** [Depth Anything 3](https://github.com/ByteDance-Seed/Depth-Anything-3) (`da3-giant`, ViT-Giant), repurposed for cubemap-based multi-view processing of the panorama.
- **Inputs:** A single ERP panorama, internally projected onto a 6-face cubemap at 504 px per face.
- **Output:** Dense surface-normal map at panoramic resolution, returned as unit vectors in the panorama's world frame.
- **Resolution:** Designed for high-resolution ERP inputs, up to 3K.
- **License:** [CC BY-NC 4.0](LICENSE) — academic / non-commercial use only. The released weights are derivative works of the [Depth Anything 3](https://github.com/ByteDance-Seed/Depth-Anything-3) `da3-giant` backbone, released by ByteDance under CC BY-NC 4.0, and inherit that restriction. Commercial use is not permitted.
- **Resources for more information:** [Project Website](https://pager360.github.io/), [Paper](https://arxiv.org/abs/2605.26368), [Code](https://github.com/prs-eth/PaGeR).

### Other released checkpoints

| Checkpoint | Hugging Face id | Depth | Normals | Sky |
|---|---|---|---|---|
| PaGeR *(unified, recommended)* | [`prs-eth/PaGeR`](https://huggingface.co/prs-eth/PaGeR) | ✅ | ✅ | ✅ |
| PaGeR-Metric-Depth | [`prs-eth/PaGeR-metric-depth`](https://huggingface.co/prs-eth/PaGeR-metric-depth) | ✅ (metric) |  |  |
| **PaGeR-Normals** *(this card)* | [`prs-eth/PaGeR-normals`](https://huggingface.co/prs-eth/PaGeR-normals) |  | ✅ |  |

## Usage

A minimal Python snippet that runs the normals-only model on a single panorama and produces a dense surface-normal map in one forward pass. The snippet assumes you have [cloned the repository](https://github.com/prs-eth/PaGeR) and `pip install -e .` ed it, so that `src.pager` is importable; checkpoint weights and config are streamed from the Hub on first use.

```python
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from omegaconf import OmegaConf
from PIL import Image

from src.pager import Pager
from src.utils.geometry_utils import erp_to_cubemap
from src.utils.utils import prepare_normals_for_logging

checkpoint = "prs-eth/PaGeR-normals"
device = torch.device("cuda")

# 1. Load the model config from the Hub and instantiate Pager.
config_path = hf_hub_download(repo_id=checkpoint, filename="config.yaml")
cfg = OmegaConf.load(config_path)

pager = Pager(checkpoint, cfg=cfg, device=device)
pager.get_intrinsics_extrinsics(image_size=cfg.face_size, fov=getattr(cfg, "cube_fov", 90.0))
pager.model.to(device).eval()

# 2. Load a panorama and project it to the 6-face cubemap PaGeR consumes.
panorama = np.array(Image.open("assets/examples/apartment_synth.jpg").convert("RGB")) / 255.0
panorama = torch.from_numpy(panorama).permute(2, 0, 1).float() * 2 - 1
rgb_cubemap = erp_to_cubemap(panorama, face_w=cfg.face_size,
                             fov=getattr(cfg, "cube_fov", 90.0)).unsqueeze(0).to(device)

# 3. Run one forward pass. The normals head is the only head in this
#    checkpoint — no depth, sky, or scale outputs.
with torch.inference_mode():
    pred = pager(rgb_cubemap, dtype=torch.float16)

# 4. Convert the raw normals output into an ERP-resolution unit-normal map.
#    ``sky_mask=None`` because this checkpoint has no sky head, so unbounded
#    regions are left as predicted instead of being masked out.
H, W = panorama.shape[-2:]
normals, normals_viz = prepare_normals_for_logging(
    pager, pred["normals"][0], None, (H, W),
)
```

`normals` is a `(3, H, W)` float32 unit-normal field in the panorama's world frame at the input panorama resolution; `normals_viz` is the uint8 RGB preview (per-sample rescaled). If you also need sky filling, depth, or metric scale from the same model, use the unified [`prs-eth/PaGeR`](https://huggingface.co/prs-eth/PaGeR) checkpoint instead. See the [GitHub repository](https://github.com/prs-eth/PaGeR) for the full CLI (`inference.py`), evaluation scripts, the Gradio demo (`app.py`), and the point-cloud exporter.

## Citation

If you use this checkpoint in your work, please cite:

```bibtex
@article{bozic2026pager,
  title   = {Unified Panoramic Geometry Estimation via Multi-View Foundation Models},
  author  = {Bozic, Vukasin and Slavkovic, Isidora and Narnhofer, Dominik and
             Metzger, Nando and Rozumny, Denis and Schindler, Konrad and
             Kalischek, Nikolai},
  journal = {arXiv preprint arXiv:2605.26368},
  year    = {2026}
}
```