collector-m / astar-3d

An Autonomous Driving Dataset from I2R, A*STAR

A*3D: An Autonomous Driving Dataset in Challeging Environments

Introduction

A*3D dataset is a step forward to make autonomous driving safer for pedestrians and the public in the real world.

• 230K human-labeled 3D object annotations in 39,179 LiDAR point cloud frames and corresponding frontal-facing RGB images.
• Captured at different times (day, night) and weathers (sun, cloud, rain).

Night-time high-density examples from the proposed A*3D dataset with RGB images and their corresponding LiDAR data.

Sensor Setup

We collect raw sensor data using the A*STAR autonomous vehicle, which is equipped with the following sensors:

• Two PointGrey Chameleon3 USB3 Global shutter color cameras (CM3-U3-31S4C-CS) with 55Hz frame rate, 2048 × 1536 resolution.
• A Velodyne HDL-64ES3 3D-LiDAR with 10Hz spinrate, 64 laser beams.

The following depicts the Sensor setup for A*3D data collection vehicle platform.

Data Collection

• The data collection covers the entire Singapore including highways, neighborhood roads, tunnels, urban, suburban, industrial, HDB car parks, coastline, etc.

• NuScenes only covers a small portion of Singapore roads (highlighted in red).

  

Dataset Statistics

• 17% frames with high object-density.
  • The number of annotations per frame for A*3D dataset are much higher than KITTI dataset.
  • The A*3D dataset comprises 7 annotated classes corresponding to the most common objects in road scenes.

• 25% frames with heavy occlusion.

  • About half of the vehicles are partially or highly occluded.
  
  • Average number of points inside the bounding box of each class and the Log number of points within bounding box.
  

• Radial distance.

  

• Distribution of object orientation.

  

• Box dimensions.

  

Benchmarking

• Object-density: Cross-dataset Evaluation

  • A pre-trained model of PointRCNN on KITTI suffers almost a 15% drop in mAP on A*3D validation set.
  • When trained on our high-density subset, PointRCNN achieves much better performance on the KITTI validation set, especially on Moderate and Hard with almost 10% improvements.
  

• High object-density vs. Low object-density

  • When increasing the training data, the performance improvements are marginal.
  • The best result comes from mixing high and low density samples.
    

• Day-time vs. Night-time

  • We are the first to provide a systematic study on the effects of night-time on 3D object detection systems with F-PointNet and AVOD methods.
    

Download A*3D dataset

Please email Jie Lin ([email protected]) for the download link to the dataset. Note that this dataset is for non-commercial research purposes only. A Non-Commercial Use Agreement needs to be signed.

Citation

If using our data in your research work, please cite the following paper:

@article{astar-3d,
	author = {Quang-Hieu Pham, Pierre Sevestre, Ramanpreet Singh Pahwa, Huijing Zhan, Chun Ho Pang, Yuda Chen, Armin Mustafa, Vijay Chandrasekhar, Jie Lin},
	title = {A*3D Dataset: Towards Autonomous Driving in Challenging Environments},  
	year = {2019},
	eprint = {1909.07541}
}