A*3D Dataset: Towards Autonomous Driving in Challenging Environments
This dataset fills gaps for researchers in autonomous driving by providing more diverse and challenging scenarios, though it is incremental as it builds on existing datasets.
The authors introduced the A*3D dataset to address the lack of challenging real-world data for autonomous driving, featuring 39K frames with high-density images, heavy occlusions, and significant night-time content, and benchmarked 3D object detection to reveal insights into performance in diverse environments.
With the increasing global popularity of self-driving cars, there is an immediate need for challenging real-world datasets for benchmarking and training various computer vision tasks such as 3D object detection. Existing datasets either represent simple scenarios or provide only day-time data. In this paper, we introduce a new challenging A*3D dataset which consists of RGB images and LiDAR data with significant diversity of scene, time, and weather. The dataset consists of high-density images ($\approx~10$ times more than the pioneering KITTI dataset), heavy occlusions, a large number of night-time frames ($\approx~3$ times the nuScenes dataset), addressing the gaps in the existing datasets to push the boundaries of tasks in autonomous driving research to more challenging highly diverse environments. The dataset contains $39\text{K}$ frames, $7$ classes, and $230\text{K}$ 3D object annotations. An extensive 3D object detection benchmark evaluation on the A*3D dataset for various attributes such as high density, day-time/night-time, gives interesting insights into the advantages and limitations of training and testing 3D object detection in real-world setting.