Dataset (Click here to download)

The large indoor and outdoor scenes in our dataset. Left: a climbing gym (1200 m2). Middle: a lab building with an outside courtyard 4000 m2. Right: a loop road scene 4600 m2

Data structure

Dataset root/
├── [Place_holder]/
|  ├── [Place_holder].bvh     # MoCap data from Noitom Axis Studio (PNStudio)
|  ├── [Place_holder]_pos.csv # Every joint's roration, generated from `*_bvh`
|  ├── [Place_holder]_rot.csv # Every joint's translation, generated from `*_bvh`
|  ├── [Place_holder].pcap    # Raw data from the LiDAR
|  └── [Place_holder]_lidar_trajectory.txt  # N×9 format file
├── ...
|
└── scenes/
   ├── [Place_holder].pcd
   ├── [Place_holder]_ground.pcd
   ├── ...
   └── ...
  1. Place_holder can be replaced to campus_raod, climbing_gym, and lab_building.
  2. *_lidar_trajectory.txt is generated by our Mapping method and manually calibrated with corresponding scenes.
  3. *_bvh and *_pcap are raw data from sensors. They will not be used in the following steps.
  4. You can test your SLAM algorithm by using *_pcap captured from Ouster1-64 with 1024×20Hz.

The code base is here.

The HSC4D dataset is published under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.You must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. Contact us if you are interested in commercial usage.

Citation

@misc{dai2022hsc4d,
    title={HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR},
    author={Yudi Dai and Yitai Lin and Chenglu Wen and Siqi Shen and Lan Xu and Jingyi Yu and Yuexin Ma and Cheng Wang},
    year={2022},
    eprint={2203.09215},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}