Blame for README.md

---
license: mit
pretty_name: The Multiview Moving Camera Dataset (MMCD)
viewer: false
source_datasets:
  - original
size_categories:
  - n<1K
tags:
  - video-stitching
  - image-stitching
  - multi-view
  - moving-cameras
  - panoramic-video
  - keypoints
---

# The Multiview Moving Camera Dataset (MMCD)

## Dataset Summary

To our knowledge, no available dataset supports multiple video stitching with *N* > 2 freely moving cameras.
Thus, we created MMCD, a new multiview dataset. MMCD contains 53 scenes, each with 3–6 approximately
synchronized handheld videos captured on diverse mobile devices under varied environmental and lighting conditions.
Camera models, resolutions, and motions vary substantially. Overall, MMCD contains 221 videos and 47,040 frames.

To facilitate quantitative evaluation, we manually annotated keypoints in the overlap regions of the first frames
and propagated them through the sequences using CoTracker3. The annotations are used solely for evaluation.

The dataset is provided as a single file, `MMCD.zip`. Unzipping it gives the following structure:

```
MMCD/
├── frames/
│   └── <scene>/
│       ├── video0/
│       ├── video1/
│       └── ...
└── kps_annotations/
    └── <scene>/
        ├── kps_<scene>.pt
        └── pred_visibility_<scene>.pt
```

*N* - the number of videos in the scene.

## Data Fields

In addition to the frames folder, each scene has 2 tensors, one for the keypoint annotations and one for the visibility flags.

- `frames/<scene>/video<i>/`: the frames of video *i* of the scene.
- `kps_annotations/<scene>/kps_<scene>.pt`: a PyTorch tensor of shape `[num_frames, N, keypoint_index, 2]`, with the 2D position of every
  keypoint in every frame of every video, in pixels of the original frames.
- `kps_annotations/<scene>/pred_visibility_<scene>.pt`: a PyTorch tensor of shape `[num_frames, N, keypoint_index]`, with the visibility
  flags predicted by CoTracker3: 1 if the keypoint is visible in that video's frame, and 0 otherwise.

## Data Statistics

MMCD has no train/test splits; all of the scenes are used for evaluation. It is divided into 4 categories based on
*N*, the number of cameras:

| | *N* = 3 | *N* = 4 | *N* = 5 | *N* = 6 | Total |
| --- | :---: | :---: | :---: | :---: | :---: |
| Scenes | 22 | 10 | 11 | 10 | 53 |

All of the videos are 30 fps.

<details>
<summary><b>N = 3</b> &nbsp;(22 scenes)</summary>

| Scene | Length | Resolution (H×W) |
| --- | :---: | :---: |
| scene_1 | 9 sec | 478×848 |
| scene_2 | 6 sec | 478×848 |
| scene_4 | 7 sec | 478×848 |
| scene_6 | 8 sec | 478×848 |
| scene_7 | 9 sec | 478×848 |
| scene_8 | 10 sec | 478×848 |
| scene_9 | 6 sec | 478×848 |
| scene_10 | 7 sec | 478×848 |
| scene_12 | 8 sec | 478×848 |
| scene_13 | 9 sec | 478×848 |
| scene_15 | 10 sec | 478×848 |
| scene_18 | 7 sec | 478×848 |
| scene_19 | 8 sec | 478×848 |
| scene_20 | 9 sec | 478×848 |
| scene_21 | 10 sec | 478×848 |
| scene_29 | 6 sec | 480×840 |
| scene_39 | 10 sec | 480×840 |
| scene_67 | 10 sec | 480×840 |
| scene_69 | 9 sec | 480×840 |
| scene_70 | 10 sec | 480×840 |
| scene_106 | 5 sec | 480×840 |
| scene_110 | 9 sec | 1200×800 |

</details>

<details>
<summary><b>N = 4</b> &nbsp;(10 scenes)</summary>

| Scene | Length | Resolution (H×W) |
| --- | :---: | :---: |
| scene_25 | 5 sec | 480×840 |
| scene_30 | 5 sec | 480×840 |
| scene_31 | 6 sec | 480×840 |
| scene_35 | 6 sec | 480×840 |
| scene_47 | 7 sec | 480×840 |
| scene_49 | 5 sec | 480×840 |
| scene_54 | 8 sec | 480×840 |
| scene_64 | 6 sec | 480×840 |
| scene_81 | 8 sec | 480×840 |
| scene_92 | 10 sec | 450×478 |

</details>

<details>
<summary><b>N = 5</b> &nbsp;(11 scenes)</summary>

| Scene | Length | Resolution (H×W) |
| --- | :---: | :---: |
| scene_33 | 7 sec | 480×840 |
| scene_46 | 8 sec | 480×840 |
| scene_51 | 7 sec | 480×840 |
| scene_62 | 9 sec | 480×840 |
| scene_63 | 7 sec | 480×840 |
| scene_65 | 5 sec | 480×840 |
| scene_66 | 7 sec | 480×840 |
| scene_72 | 8 sec | 480×840 |
| scene_76 | 6 sec | 480×840 |
| scene_77 | 6 sec | 480×840 |
| scene_98 | 6 sec | 480×840 |

</details>

<details>
<summary><b>N = 6</b> &nbsp;(10 scenes)</summary>

| Scene | Length | Resolution (H×W) |
| --- | :---: | :---: |
| scene_27 | 7 sec | 480×840 |
| scene_50 | 5 sec | 480×840 |
| scene_52 | 6 sec | 480×840 |
| scene_53 | 5 sec | 480×840 |
| scene_55 | 7 sec | 480×840 |
| scene_57 | 6 sec | 480×840 |
| scene_58 | 7 sec | 480×840 |
| scene_59 | 6 sec | 480×840 |
| scene_60 | 5 sec | 480×840 |
| scene_101 | 9 sec | 480×840 |

</details>

## Personal and Sensitive Information

The dataset does not contain personal or sensitive information.

## Citation

```bibtex
@inproceedings{Ifergane:NeurIPS:2026:MViS,
  title     = {Video Stitching from Multiple Moving Cameras},
  author    = {Ifergane, Shira and Amoyal, Roy and Benishay, Shahar and Freifeld, Oren},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year      = {2026}
}
```