  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.
