|
|
--- |
|
|
license: mit |
|
|
task_categories: |
|
|
- robotics |
|
|
- computer-vision |
|
|
tags: |
|
|
- robotics |
|
|
- optical-flow |
|
|
- scene-flow |
|
|
- 3d-vision |
|
|
- manipulation |
|
|
size_categories: |
|
|
- 10K<n<100K |
|
|
--- |
|
|
|
|
|
# 3D Optical Flow DROID Dataset |
|
|
|
|
|
Processed DROID robotics dataset with optical flow and scene flow annotations. |
|
|
|
|
|
## Dataset Structure |
|
|
|
|
|
Organized by lab, each trajectory in separate tar.gz archive: |
|
|
|
|
|
``` |
|
|
IPRL/IPRL+2023-06-19+Mon_Jun_19_23:27:48_2023.tar.gz |
|
|
CLVR/CLVR+2023-...tar.gz |
|
|
... (15 labs, ~33K trajectories) |
|
|
``` |
|
|
|
|
|
Each trajectory contains: |
|
|
- `metadata.json` - Trajectory metadata |
|
|
- `trajectory.h5` - Robot state and actions |
|
|
- `camera_left/`, `camera_right/` - Camera data |
|
|
- `rgb/` - RGB images |
|
|
- `depth/` - Depth maps |
|
|
- `optical_flow_with_mask/` - 2D optical flow |
|
|
- `scene_flow/` - 3D scene flow |
|
|
|
|
|
## Usage |
|
|
|
|
|
```python |
|
|
from huggingface_hub import hf_hub_download |
|
|
import tarfile |
|
|
|
|
|
# Download specific trajectory |
|
|
tar_path = hf_hub_download( |
|
|
repo_id="Salesforce/3d_optical_flow_droid", |
|
|
filename="IPRL/IPRL+2023-06-19+Mon_Jun_19_23:27:48_2023.tar.gz", |
|
|
repo_type="dataset" |
|
|
) |
|
|
|
|
|
# Extract |
|
|
with tarfile.open(tar_path, "r:gz") as tar: |
|
|
tar.extractall("./data") |
|
|
``` |
|
|
|
|
|
## Stats |
|
|
|
|
|
- Trajectories: ~33,108 |
|
|
- Size: ~26 TB (compressed) |
|
|
- Labs: 15 robotics labs |
|
|
- Frames: ~600-700 per trajectory |
|
|
|
|
|
## Citation |
|
|
|
|
|
```bibtex |
|
|
@article{droid2024, |
|
|
title={DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset}, |
|
|
year={2024} |
|
|
} |
|
|
``` |
|
|
|
