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 a significant diversity of scene, time, and weather. The dataset consists of high-density images (~10 times more than the pioneering KITTI dataset), heavy occlusions, a large number of nighttime frames (~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 39K frames, 7 classes, and 230K 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 a real-world setting.
This work is supported by the Agency for Science, Technology and Research (A∗STAR) under its AME Programmatic Funds (ProjectNo.A1892b0026).