PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving
This dataset addresses the need for high-quality, labeled real-world data to train deep learning networks for self-driving perception, but it is incremental as it builds on existing dataset efforts.
The authors introduced PandaSet, a dataset for autonomous driving that includes over 100 scenes with 8-second durations, providing 28 object classification and 37 semantic segmentation labels, and established baselines for 3D object detection and segmentation tasks.
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° 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.