PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving (2112.12610v1)
Abstract: The accelerating development of autonomous driving technology has placed greater demands on obtaining large amounts of high-quality data. Representative, labeled, real world data serves as the fuel for training deep learning networks, critical for improving self-driving perception algorithms. In this paper, we introduce PandaSet, the first dataset produced by a complete, high-precision autonomous vehicle sensor kit with a no-cost commercial license. The dataset was collected using one 360{\deg} mechanical spinning LiDAR, one forward-facing, long-range LiDAR, and 6 cameras. The dataset contains more than 100 scenes, each of which is 8 seconds long, and provides 28 types of labels for object classification and 37 types of labels for semantic segmentation. We provide baselines for LiDAR-only 3D object detection, LiDAR-camera fusion 3D object detection and LiDAR point cloud segmentation. For more details about PandaSet and the development kit, see https://scale.com/open-datasets/pandaset.
- Pengchuan Xiao (2 papers)
- Zhenlei Shao (2 papers)
- Steven Hao (3 papers)
- Zishuo Zhang (1 paper)
- Xiaolin Chai (1 paper)
- Judy Jiao (1 paper)
- Zesong Li (2 papers)
- Jian Wu (314 papers)
- Kai Sun (317 papers)
- Kun Jiang (128 papers)
- Yunlong Wang (91 papers)
- Diange Yang (37 papers)