Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ITSC48978.2021.9565009 | ITSC |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 12 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengchuan Xiao | 1 | 0 | 0.34 |
| Zhenlei Shao | 2 | 0 | 0.34 |
| Steven Hao | 3 | 0 | 0.34 |
| Zishuo Zhang | 4 | 0 | 0.34 |
| Xiaolin Chai | 5 | 0 | 1.01 |
| Judy Jiao | 6 | 0 | 0.34 |
| Zesong Li | 7 | 0 | 0.34 |
| Jian Wu | 8 | 933 | 95.62 |
| Kai Sun | 9 | 33 | 7.71 |
| kun jiang | 10 | 11 | 7.72 |
| Yunlong Wang | 11 | 55 | 11.52 |
| Diange Yang | 12 | 33 | 13.12 |