README.md

---
license: mit
task_categories:
  - image-segmentation
  - image-feature-extraction
tags:
  - covisibility
  - pose-estimation
  - croco
  - alligat0r
  - nuscenes
  - scannet
  - neurips-2025
size_categories:
  - 1M<n<10M
pretty_name: "Cub3: Covisibility-Annotated Pairs from nuScenes and ScanNet"
---

# Cub3: A Large-Scale Covisibility Dataset

Cub3 is a large-scale dataset comprising **5 million image pairs** with dense, pixel-level covisibility annotations derived from the autonomous driving [nuScenes](https://www.nuscenes.org/) dataset and the indoor [ScanNet](http://www.scan-net.org/) dataset. It was introduced alongside **Alligat0R** ([arXiv:2503.07561](https://arxiv.org/abs/2503.07561)), a NeurIPS 2025 Spotlight paper.

## Covisibility Classes

Each pixel in a view is classified into one of three categories with respect to the other view:

| Class | Label | Description |
|---|---|---|
| **Covisible** | 0 (pixel value 255) | The 3D point is visible in both views |
| **Occluded** | 1 (pixel value 128) | The 3D point is occluded in the other view |
| **Outside FOV** | 2 (pixel value 0) | The 3D point is outside the field of view of the other view |

## Dataset Variants

| Split | Overlap threshold | nuScenes pairs | ScanNet pairs | Total |
|---|---|---|---|---|
| **Cub3-50** | >= 50% | 2.5M | 2.5M | 5M |
| **Cub3-all** | >= 5% | 2.5M | 2.5M | 5M |

## Repository Structure

```
├── nuscenes/
│   ├── train_samples_50.json        # Pair metadata for Cub3-50
│   ├── train_samples_all.json       # Pair metadata for Cub3-all
│   ├── train_seg_50/                # Segmentation masks (10 shards)
│   │   ├── scene-00.tar.gz
│   │   └── ...
│   └── train_seg_all/               # Segmentation masks (10 shards)
│       ├── scene-00.tar.gz
│       └── ...
└── scannet/
    ├── train_samples_50.json
    ├── train_samples_all.json
    ├── scene00.tar.gz               # Segmentation masks (8 shards)
    └── ...
```

## Pair Metadata Format

### nuScenes (`train_samples_{50,all}.json`)

Each entry is a list: `[img1_name, img2_name, scene, relative_pose_4x4, overlap, angle, dist_ratio]`

- `img1_name` / `img2_name`: nuScenes sweep filenames (e.g. `n008-2018-...CAM_FRONT__153565...jpg`)
- `scene`: nuScenes scene id (e.g. `scene-0527`)
- `relative_pose_4x4`: 4x4 relative pose matrix from camera 1 to camera 2
- `overlap`: fraction of covisible pixels
- `angle`: viewpoint angle between the two cameras (degrees)
- `dist_ratio`: scale ratio between the two views

### ScanNet (`train_samples_{50,all}.json`)

Each entry is a list: `[scene, img1_name, img2_name, overlap, angle, dist_ratio, relative_pose_4x4]`

## Segmentation Mask Format

Masks are stored as grayscale PNG images named `<IMG1>+<IMG2>.png`:

- **Pixel value 255** = covisible
- **Pixel value 128** = occluded
- **Pixel value 0** = outside field of view
- **Pixel value 42** (ScanNet only) = undefined / ignored during training

## Download and Setup

```bash
pip install huggingface_hub[cli]

# Download metadata
huggingface-cli download thibautloiseau/Cub3 --include "nuscenes/train_samples_*.json" --local-dir data/Cub3
huggingface-cli download thibautloiseau/Cub3 --include "scannet/train_samples_*.json" --local-dir data/Cub3

# Download segmentation masks
huggingface-cli download thibautloiseau/Cub3 --include "nuscenes/train_seg_50/*.tar.gz" --local-dir data/Cub3
huggingface-cli download thibautloiseau/Cub3 --include "nuscenes/train_seg_all/*.tar.gz" --local-dir data/Cub3
huggingface-cli download thibautloiseau/Cub3 --include "scannet/*.tar.gz" --local-dir data/Cub3

# Extract archives
cd data/Cub3/nuscenes/train_seg_50 && for f in *.tar.gz; do tar xzf "$f"; done && cd -
cd data/Cub3/nuscenes/train_seg_all && for f in *.tar.gz; do tar xzf "$f"; done && cd -
cd data/Cub3/scannet && for f in scene*.tar.gz; do tar xzf "$f"; done && cd -
```

Expected on-disk layout after extraction:

```
data/Cub3/
├── nuscenes/
│   ├── train_samples_50.json
│   ├── train_samples_all.json
│   ├── train_seg_50/scene-XXXX/seg_aligned/<IMG1>+<IMG2>.png
│   └── train_seg_all/scene-XXXX/seg_aligned/<IMG1>+<IMG2>.png
└── scannet/
    ├── train_samples_50.json
    ├── train_samples_all.json
    └── sceneXXXX_XX/segs/<IMG1>+<IMG2>.png
```

## Important Notes

- **Raw images are NOT included.** You must obtain the original images from [nuScenes](https://www.nuscenes.org/) and [ScanNet](http://www.scan-net.org/) under their respective licenses.
- The nuScenes annotations are generated using monocular depth predictions (UniDepth) and surface normals (Depth Anything V2) combined with COLMAP poses, following the pipeline from [RUBIK](https://github.com/thibautloiseau/RUBIK). Some annotations may contain noise, particularly the distinction between covisible and occluded pixels.
- The ScanNet annotations use ground-truth depth maps and camera poses.

## Usage with Alligat0R

See the [Alligat0R GitHub repository](https://github.com/thibautloiseau/alligat0r) for training scripts that consume this dataset directly, and [`demo.py`](https://github.com/thibautloiseau/alligat0r/blob/main/demo.py) for a quick covisibility visualization example.

## Citation

```bibtex
@article{loiseau2026alligat0r,
  title={Alligat0r: Pre-training through covisibility segmentation for relative camera pose regression},
  author={Loiseau, Thibaut and Bourmaud, Guillaume and Lepetit, Vincent},
  journal={Advances in Neural Information Processing Systems},
  volume={38},
  pages={13762--13789},
  year={2026}
}
```

## License

The Cub3 annotations are released under the [MIT License](https://opensource.org/licenses/MIT). The underlying images from nuScenes and ScanNet are subject to their own licenses.