README.md

---
license: cc-by-nc-4.0
pretty_name: OmniRooms
task_categories:
- depth-estimation
- image-to-3d
tags:
- panorama
- depth
- synthetic
- indoor-scene
- 3d
- vla
size_categories:
- 100K<n<1M
configs:
- config_name: rgb_images
  default: true
  drop_labels: true
  data_files:
  - split: train
    path:
    - "AI*/Original/*.jpg"
    - "AIUE*/Original/*.jpg"
    - "AIUE5_vol8_04/fov130_fisheye/images/*.jpg"
---

# 🏠 OmniRooms

OmniRooms is a large-scale synthetic indoor dataset for panoramic 3D perception, depth estimation, and monocular view synthesis. It is designed for research on 3D reconstruction and 3D Gaussian Splatting from universal camera inputs, especially equirectangular panoramas and wide-FoV fisheye images. 

The dataset contains 16 large indoor scenes and each scene contains multiple rooms. In total, the current release contains 271k equirectangular RGB panoramas and paired depth maps, plus 11,880 derived fisheye images for OmniRooms-Wide. 

# 🖼️ Scene Preview
## 🏘️ 16 Scenes
<div style="display: grid; grid-template-columns: repeat(4, 1fr); gap: 4px; background: transparent; margin: 0; padding: 0; line-height: 0;">
  <img src="assets/dataset/AIUE5_vol8_03_2x2.jpg" alt="OmniRooms scene AIUE5 vol8 03" style="width: 100%; height: auto; object-fit: contain; margin: 0; padding: 0; display: block;">
  <img src="assets/dataset/AIUE5_vol8_04_2x2.jpg" alt="OmniRooms scene AIUE5 vol8 04" style="width: 100%; height: auto; object-fit: contain; margin: 0; padding: 0; display: block;">
  <img src="assets/dataset/AIUE5_vol8_05_2x2.jpg" alt="OmniRooms scene AIUE5 vol8 05" style="width: 100%; height: auto; object-fit: contain; margin: 0; padding: 0; display: block;">
  <img src="assets/dataset/AIUE_V01_001_2x2.jpg" alt="OmniRooms scene AIUE V01 001" style="width: 100%; height: auto; object-fit: contain; margin: 0; padding: 0; display: block;">

  <img src="assets/dataset/AIUE_V01_003_2x2.jpg" alt="OmniRooms scene AIUE V01 003" style="width: 100%; height: auto; object-fit: contain; margin: 0; padding: 0; display: block;">
  <img src="assets/dataset/AIUE_V01_004_2x2.jpg" alt="OmniRooms scene AIUE V01 004" style="width: 100%; height: auto; object-fit: contain; margin: 0; padding: 0; display: block;">
  <img src="assets/dataset/AIUE_V02_001_2x2.jpg" alt="OmniRooms scene AIUE V02 001" style="width: 100%; height: auto; object-fit: contain; margin: 0; padding: 0; display: block;">
  <img src="assets/dataset/AI_vol3_01_2x2.jpg" alt="OmniRooms scene AI vol3 01" style="width: 100%; height: auto; object-fit: contain; margin: 0; padding: 0; display: block;">

  <img src="assets/dataset/AI_vol3_02_2x2.jpg" alt="OmniRooms scene AI vol3 02" style="width: 100%; height: auto; object-fit: contain; margin: 0; padding: 0; display: block;">
  <img src="assets/dataset/AI_vol3_03_2x2.jpg" alt="OmniRooms scene AI vol3 03" style="width: 100%; height: auto; object-fit: contain; margin: 0; padding: 0; display: block;">
  <img src="assets/dataset/AI_vol3_04_2x2.jpg" alt="OmniRooms scene AI vol3 04" style="width: 100%; height: auto; object-fit: contain; margin: 0; padding: 0; display: block;">
  <img src="assets/dataset/AI_vol4_01_2x2.jpg" alt="OmniRooms scene AI vol4 01" style="width: 100%; height: auto; object-fit: contain; margin: 0; padding: 0; display: block;">

  <img src="assets/dataset/AI_vol4_02_2x2.jpg" alt="OmniRooms scene AI vol4 02" style="width: 100%; height: auto; object-fit: contain; margin: 0; padding: 0; display: block;">
  <img src="assets/dataset/AI_vol4_03_2x2.jpg" alt="OmniRooms scene AI vol4 03" style="width: 100%; height: auto; object-fit: contain; margin: 0; padding: 0; display: block;">
  <img src="assets/dataset/AI_vol4_04_2x2.jpg" alt="OmniRooms scene AI vol4 04" style="width: 100%; height: auto; object-fit: contain; margin: 0; padding: 0; display: block;">
  <img src="assets/dataset/AI_vol4_05_2x2.jpg" alt="OmniRooms scene AI vol4 05" style="width: 100%; height: auto; object-fit: contain; margin: 0; padding: 0; display: block;">
</div>

## 🎯 One Group
<p align="center">
  <img src="assets/dataset/AIUE5_vol8_04_8670_8699_6x5.jpg" alt="One Group" width="90%">
</p>

## ⚙️ Data Generation

OmniRooms is collected in AirSim environments. Anchor points are sampled on a 0.5 m voxel grid. For each anchor point, one central camera and 29 randomly sampled cameras within a 0.3 m-radius sphere are rendered. To isolate translation-induced synthesis controlled, all cameras share a fixed orientation in the panorama release. Because this is a panorama, you can manually create rotations through code. OmniRooms-Wide is generated by projecting OmniRooms panoramas into 130-degree equidistant fisheye views. 

Each panorama frame is rendered as a 1024 x 2048 equirectangular RGB image. Depth maps are provided at the same resolution.

## 📁 Directory Structure

The dataset root is organized by scene:

```text
OmniRooms/
├── <scene_name>/
│   ├── Original/
│   │   ├── Panoramagram0.jpg
│   │   ├── Panoramagram1.jpg
│   │   └── ...
│   └── Depth/
│       ├── Depth0.h5
│       ├── Depth1.h5
│       └── ...
└── 30cm (pose)/
    ├── AI_vol3_01.csv
    └── ...
```

### RGB panoramas

RGB panoramas are stored as JPEG files:

```text
<scene_name>/Original/Panoramagram{index}.jpg
```

Each RGB panorama is a 2048 x 1024 equirectangular image in standard RGB color space.

### Depth maps

Depth files are stored as HDF5 files:

```text
<scene_name>/Depth/Depth{index}.h5
```

Each HDF5 depth file contains:

- `depth`: float32 array with shape `(1024, 2048)`.
- `alpha`: float32 array with shape `(1024, 2048)`.

### Camera positions

Camera positions are provided as CSV files:

```text
30cm/<scene_name>.csv
```

The CSV files use columns:

```text
,X,Y,Z
```

## 💻 Loading Example

The following example loads one RGB panorama and its paired depth map:

```python
from pathlib import Path

import h5py
from PIL import Image

root = Path("/path/to/OmniRooms")
scene = "AI_vol3_01"
frame_idx = 0

rgb_path = root / scene / "Original" / f"Panoramagram{frame_idx}.jpg"
depth_path = root / scene / "Depth" / f"Depth{frame_idx}.h5"

rgb = Image.open(rgb_path).convert("RGB")

with h5py.File(depth_path, "r") as f:
    depth = f["depth"][:]

print(rgb.size)       # (2048, 1024)
print(depth.shape)    # (1024, 2048)
```

## 📜 License

This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0). 

You may use the dataset for non-commercial research and education. Commercial use is not permitted without additional permission.

## 📚 Citation

If you use OmniRooms or OmniRooms-Wide in your research, please cite the corresponding UniSHARP project:

```bibtex
@article{song2026unisharp,
  title={UniSHARP: Universal Sharp Monocular View Synthesis},
  author={Song, Meixi and Zhang, Dizhe and Ren, Hao and Zhang, Ruiyang and Du, Bo and Yang, Ming-Hsuan and Qi, Lu},
  journal={arXiv},
  year={2026}
}
```