---
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-*
---
🗃️ ZüriPano Dataset
**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 (`- s`). |
| `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}
}
```