---
license: cc-by-nc-4.0
language:
- en
tags:
- computer-vision
- object-dynamics
- gaussian-splatting
- multi-view
pretty_name: GS Physics Dataset
size_categories:
- n<1K
---

# GS Physics Dataset

This dataset accompanies the CVPR 2026 Findings paper
[*Learning a Particle Dynamics Model with Real-world Videos*](https://arxiv.org/abs/2605.23845).
It contains real-world multi-object interaction recordings and processed
Gaussian Splatting particle-dynamics data for the `bowling` and `cube_stacks`
scenarios.

The release includes two archive files:

- `dataset.zip`: raw multi-view RGB/stereo/depth recordings, segmentation masks,
  camera metadata, and object-pose estimates.
- `processed_dataset.zip`: processed data used by the released training and
  evaluation code, including masked RGB images, segmentation masks, object poses,
  and per-frame Gaussian parameter files.

The official code repository is available at:
[chkim403/gs-particle-dynamics](https://github.com/chkim403/gs-particle-dynamics).

## Dataset Summary

The dataset contains 529 real-world scenes captured from four synchronized camera
views:

| Scenario | Number of scenes |
| --- | ---: |
| `bowling` | 297 |
| `cube_stacks` | 232 |
| Total | 529 |

For the experiments in the paper, 292 `bowling` scenes and 210 `cube_stacks`
scenes were used. The remaining scenes were excluded because of issues observed
in their preprocessed outputs, such as segmentation failures. The excluded-scene
list is available as `skip_list` in `tools/dataset.py` in the official code
repository.

Scene IDs are not guaranteed to be continuous because some unusable scenes were
manually removed after continuous recording.

## Files

```text
.
├── dataset.zip
└── processed_dataset.zip
```

Both archives include the dataset license and terms of use.

## Raw Dataset

`dataset.zip` contains the original released dataset files.

### Raw root structure

```text
.
├── bowling/
│   ├── scene_00001/
│   ├── scene_00002/
│   └── ...
├── cube_stacks/
│   ├── scene_00003/
│   ├── scene_00004/
│   └── ...
├── dataset_license.md
├── dataset_terms_of_use.md
└── raw_dataset.md
```

Each scene contains four camera folders:

```text
<scenario>/scene_XXXXX/
├── 234322305266/
├── 248622303451/
├── 336222300744/
└── 336522303601/
```

The same four camera IDs are used across all released scenes.

### Raw camera directory structure

Each camera folder contains RGB images, stereo images, stereo depth aligned to
RGB pixels, segmentation masks, object poses, and per-camera metadata:

```text
<scenario>/scene_XXXXX/<camera_id>/
├── camera_meta.json
├── obj_poses.npz
├── rgb/
├── left/
├── right/
├── stereo_aligned_depth/
└── filtered_segmentation_DAM4SAM/
```

`camera_meta.json` is a per-camera JSON file:

```json
{
  "h": 480,
  "w": 640,
  "k": [[...], [...], [...]],
  "w2c": [[...], [...], [...], [...]],
  "depth_scale": 0.0010000000474974513
}
```

Fields:

- `h`, `w`: image height and width.
- `k`: 3x3 RGB camera intrinsic matrix.
- `w2c`: 4x4 world-to-camera extrinsic matrix.
- `depth_scale`: multiplier for converting stored depth values to metric depth,
  generated from the Intel RealSense API.

Additional raw files:

- `obj_poses.npz`: pseudo object pose information for the corresponding camera.
- `rgb/`, `left/`, `right/`: zero-padded `.jpg` image sequences.
- `stereo_aligned_depth/`: zero-padded `.npy` depth arrays aligned to the RGB
  camera.
- `filtered_segmentation_DAM4SAM/`: zero-padded `.png` segmentation masks.

RGB, stereo, and depth streams contain full camera sequences. Segmentation masks
cover selected interaction ranges chosen by human annotators and may contain
fewer frames than the full 120-frame RGB/depth streams.

Segmentation, depth, and object poses were generated by external algorithms
described in the paper and should be treated as pseudo annotations rather than
perfect ground truth. Object IDs in segmentation masks are intended to be
consistent across time and views, but masks may contain noise from tracking and
cross-view association.

## Processed Dataset

`processed_dataset.zip` contains the data required by the released training and
evaluation code.

### Processed root structure

```text
.
├── bowling/
│   ├── train/
│   └── test/
├── cube_stacks/
│   ├── train/
│   └── test/
├── dataset_license.md
├── dataset_terms_of_use.md
└── processed_dataset.md
```

### Processed scene structure

Each processed scene has scene-level camera/image metadata plus a `gs/` folder
containing per-frame Gaussian Splatting parameter files:

```text
<scenario>/<split>/scene_XXXXX/
├── camera_meta.json
├── rgb/
├── seg/
├── obj_poses/
└── gs/
    ├── <frame>/
    │   ├── params_coarse.npz
    │   └── gs_soft_ids_coarse.npz
    └── ...
```

`<split>` is either `train` or `test`.

`camera_meta.json` contains camera metadata shared by the scene:

```json
{
  "h": 480,
  "w": 640,
  "cam_id": [
    "234322305266",
    "248622303451",
    "336222300744",
    "336522303601"
  ],
  "k": [...],
  "w2c": [...]
}
```

Processed scene files:

- `rgb/<camera_id>/*.png`: masked RGB images that keep only foreground objects.
- `seg/<camera_id>/*.png`: segmentation masks used to produce the masked RGB
  images.
- `obj_poses/<camera_id>/obj_poses.npz`: per-camera object pose data.
- `gs/<frame>/params_coarse.npz`: Gaussian parameters for that frame.
- `gs/<frame>/gs_soft_ids_coarse.npz`: object-ID assignments for the Gaussians.

The `rgb/` and `seg/` folders contain the selected interaction frame range. They
contain three more frames than the corresponding `gs/` folder, with the extra
frames appended at the end of the sequence. Gaussian parameters are not stored
for these final frames because the input Gaussian trajectories used by the model
are generated by applying the transformations stored in `obj_poses/` to static
Gaussians.


## License

Unless otherwise stated, the dataset is licensed under the Creative Commons
Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). Code,
scripts, and software in the official repository are licensed separately under
the MIT License.

By downloading, accessing, or using the dataset, you are responsible for
complying with the dataset license, the dataset terms of use, and all applicable
laws and regulations.

## Citation

If you use this dataset in a publication, project, benchmark, or released model,
please cite:

```bibtex
@inproceedings{kim2026learning,
  title     = {Learning a Particle Dynamics Model with Real-world Videos},
  author    = {Kim, Chanho and Sumukh, Suhas V. and Fuxin, Li},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Findings},
  year      = {2026}
}
```

## Contact

For questions, please contact:

Chanho Kim  
kimchanh@oregonstate.edu