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Salesforce/3d_optical_flow_droid

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… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/3d_optical_flow_droid.

sourceHugging Facemitupdated 8mo agoView on Hugging Face
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Dataset Card

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}
}
Salesforce/3d_optical_flow_droid · CoolFace