H3D

Honda 3D Dataset

Introduction

The H3D is a large scale full-surround 3D multi-object detection and tracking dataset. It is gathered from HDD dataset, a large scale naturalistic driving dataset collected in San Francisco Bay Area. H3D consists of following features:

  • Full 360 degree LiDAR dataset (dense pointcloud from Velodyne-64)
  • 160 crowded and highly interactive traffic scenes
  • 1,071,302 3D bounding box labels
  • 8 common classes of traffic participants (Manually annotated every 2Hz and linearly propagated for 10 Hz data)
  • Benchmarked on state-of-the art algorithms for 3D only detection and tracking algorithms.

Videos

The H3D Dataset

Data Format

DATA:

scenario_xxx: sequence of sensor data
	|
	----CAN_* (Decoded CAN DATA):
	|		|
	|		---CAN_yaw_yyy.csv: yaw (deg/s)
	|		|
	|		---CAN_vel_yyy.csv: speed (km/hr)
	|
	----gps_*(GPS+IMU DATA):
	|		|
	|		---gps_yyy.csv: Long_Rel,Lat_Rel,In_Height,Tilt_Roll,Tilt_Pitch,Tilt_Yaw,Vel_x,Vel_y,Vel_z,Std_Dev_x,Std_Dev_y,Std_Dev_z,Std_Dev_roll,Std_Dev_pitch,Std_Dev_yaw,Std_Dev_vel_x,Std_Dev_vel_y,Std_Dev_vel_z,Abs_Lat,Abs_Long
	|			
	|
	----labels_*(labels) [c: center, l: length]:
	|		|						
	|		|
	|		---labels_3d1_yyy.txt: Full 360 deg pointcloud (label, trackerID, state[static/dynamic], c_x, c_y, c_z, l_x, l_y, l_z, yaw) 3D bounding box labeled in velodyne frame
	|							
	|
	----pointcloud* (pointcloud):
			|
			---pointcloud1_yyy.ply: Full 360 deg pointcloud (surfel format, fields: xyz, radius->intensity, confidence->ring_number, curvature->encoder_angle)

Citation

This dataset corresponds to the paper, 'The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes', as it appears in the proceedings of International Conference on Robotics and Automation (ICRA) 2019. In the current release, the data is available for researchers from universities.

The process to obtain the dataset is as follows:

  • Please send an email at  using your official university email address.   
  • 'Data Sharing Agreement'  will be sent to you to be signed by a university representative (e.g. a university professor).
  • On review, instructions for downloading the dataset will be provided.

Please cite the following paper if you find the dataset useful in your work:

@inproceedings{360LiDARTracking_ICRA_2019,
    author = {Abhishek Patil and Srikanth Malla and Haiming Gang and Yi-Ting Chen},
    title = {The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes},  
    booktitle = {International Conference on Robotics and Automation},
    year = {2019}
}