README.md

---
license: cc-by-4.0
task_categories:
- depth-estimation
tags:
- panorama
- depth
- lidar
- outdoor
- equirectangular
- '360'
size_categories:
- n<1K
pretty_name: ZüriPano
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---

<h1 align="center" style="font-size: 2.8em; margin: 0.4em 0;">🗃️ ZüriPano Dataset</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="Hugging Face 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="Hugging Face Collection">
</a>
  <a title="License" href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
  <img src="https://img.shields.io/badge/License-CC_BY_4.0-blue.svg" alt="License">
</a>
</p>

**ZüriPano** is a real-world outdoor panoramic depth benchmark, captured with the
[Leica RTC360](https://leica-geosystems.com/products/laser-scanners/scanners/leica-rtc360)
LiDAR scanner (8K capture, 130 m effective range, HDR + automated double-scan
for transient-occlusion removal). It contains 100 equirectangular panoramas
across 11 urban locations in Zürich, each paired with a dense metric depth map
and a validity mask. It is used as the outdoor evaluation benchmark for
[PaGeR](https://pager360.github.io/).

## Dataset Summary
- **Content:** 100 outdoor scans across 11 Zürich locations, evaluation only.
- **Modality:** RGB (JPG), Depth (16-bit PNG, meters via scale factor), Validity Mask (8-bit PNG), Depth Viz (8-bit Spectral RGB PNG, preview only).
- **Resolution:** 4096 × 2048 equirectangular (ERP).
- **Use Case:** Evaluating long-range outdoor panoramic depth estimation.

## Data Structure
A single `test` split with **100 rows**, one per panorama. Each row carries:

| Column | Type | Description |
| :--- | :--- | :--- |
| `id` | `string` | Sample id (`<Location>- s<NNN>`). |
| `rgb` | `Image` | 8-bit equirectangular RGB (4096 × 2048, JPG-encoded). |
| `depth` | `Image` | 16-bit single-channel PNG, `(2048, 4096)`. Decode to **meters** as `np.asarray(img, dtype=np.float32) * (200.0 / 65535.0)`. Invalid pixels are `0.0`. |
| `depth_viz` | `Image` | 8-bit RGB PNG, Spectral-colormapped log-depth (per-sample min/max stretch, median-filtered). **Preview only — do NOT use for metrics or training; decode `depth` instead.** |
| `mask` | `Image` | 8-bit single-channel PNG, `(2048, 4096)`. Decode as `np.asarray(img, dtype=bool)` (`255` → `True`). `True` = reliable pixel; `False` = sky, no-return, or specular surface (glass façades). Always apply when computing depth metrics. |

## How to Use

```python
import numpy as np
from datasets import load_dataset

ds = load_dataset("prs-eth/ZuriPano", split="test")
sample = ds[0]

rgb   = sample["rgb"]                                                       # PIL.Image, (W=4096, H=2048)
depth = np.asarray(sample["depth"], dtype=np.float32) * (200.0 / 65535.0)   # (2048, 4096) float32, meters
mask  = np.asarray(sample["mask"], dtype=bool)                              # (2048, 4096) bool

# Always apply the mask before computing depth metrics
valid_depth = depth[mask]
```

## License

ZüriPano is released under the [Creative Commons Attribution 4.0
International License (CC BY-4.0)](https://creativecommons.org/licenses/by/4.0/).
You are free to share and adapt it for any purpose, including commercial use,
as long as you attribute the PaGeR Authors and the ZüriPano dataset.

## Citation

If you use ZüriPano in your work, please cite the PaGeR paper:

```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}
}
```