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README.md

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 ---
 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