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.
@article{arxiv.2112.12610,
title = {PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving},
author = {Pengchuan Xiao and Zhenlei Shao and Steven Hao and Zishuo Zhang and Xiaolin Chai and Judy Jiao and Zesong Li and Jian Wu and Kai Sun and Kun Jiang and Yunlong Wang and Diange Yang},
journal= {arXiv preprint arXiv:2112.12610},
year = {2021}
}
Comments
This paper has been published on ITSC'2021, please check the website of the PandaSet for more information: https://pandaset.org/