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license: bsd-3-clause
configs:
- config_name: indoor
data_files:
- split: train
path: "data/indoor/train-*"
- split: val
path: "data/indoor/val-*"
- split: test
path: "data/indoor/test-*"
- config_name: nature
data_files:
- split: train
path: "data/nature/train-*"
- split: val
path: "data/nature/val-*"
- split: test
path: "data/nature/test-*"
dataset_info:
- config_name: indoor
features:
- name: image
dtype: image
- name: depth
dtype: binary
- name: normals
dtype: binary
- config_name: nature
features:
- name: image
dtype: image
- name: depth
dtype: binary
- name: normals
dtype: binary
---
# 🗃️ Pano-Infinigen Dataset
<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/2505.09358" 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" href="https://huggingface.co/spaces/prs-eth/PaGeR" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
<img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-FFD21E" alt="Hugging Face Spaces">
</a>
<a title="License" href="https://opensource.org/licenses/BSD-3-Clause" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
<img src="https://img.shields.io/badge/License-BSD_3--Clause-blue.svg" alt="License">
</a>
</p>
**Pano-Infinigen** is a synthetic dataset of high-resolution panoramic images in [ERP](https://en.wikipedia.org/wiki/Equirectangular_projection), featuring perfectly aligned RGB, Depth, and Surface Normals. This dataset was generated using a modified [Infinigen](https://infinigen.org/) framework to support wide-angle panoramic geometry.
It serves as the primary training data for [PaGeR](https://pager360.github.io/), a single-step diffusion model for zero-shot panoramic depth and normal estimation.
## Dataset Summary
- **Content:** Synthetic indoor and outdoor(nature) scenes.
- **Modality:** RGB (PNG), Depth (binary .npy), Surface Normals (binary .npy).
- **Projection:** Equirectangular (ERP).
- **Use Case:** Training and evaluating monocular panoramic depth and normal estimation models.
## Data Structure
The dataset is split into two configurations: `indoor` and `nature`. Each contains `train`, `validation`, and `test` splits.
| Feature | Type | Description |
| :--- | :--- | :--- |
| `image` | `PIL.Image` | 8-bit RGB Panoramic Image. |
| `depth` | `binary` | **float16** NumPy array. Range: [0, 75] meters. |
| `normals` | `binary` | **float16** NumPy array. Range: [-1, 1]. |
## How to Use
Since `depth` and `normals` are stored as binary blobs to preserve precision (float16), you need to use `io.BytesIO` to load them back into NumPy.
```python
import io
import numpy as np
from datasets import load_dataset
# Load the indoor training split
ds = load_dataset("prs-eth/PanoInfinigen", name="indoor", split="train")
sample = ds[0]
# 1. Get RGB Image
rgb = sample["image"]
# 2. Convert Binary Depth to NumPy (float16, 0-75m)
depth = np.load(io.BytesIO(sample["depth"]))
# 3. Convert Binary Normals to NumPy (float16, -1 to 1)
normals = np.load(io.BytesIO(sample["normals"]))
|