PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving

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 thi...

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Vydáno v:2021 IEEE International Intelligent Transportation Systems Conference (ITSC) s. 3095 - 3101
Hlavní autoři: Xiao, Pengchuan, Shao, Zhenlei, Hao, Steven, Zhang, Zishuo, Chai, Xiaolin, Jiao, Judy, Li, Zesong, Wu, Jian, Sun, Kai, Jiang, Kun, Wang, Yunlong, Yang, Diange
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 19.09.2021
Témata:
Data collection
Laser radar
Measurement
Object detection
Semantics
Three-dimensional displays
Training
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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° 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.
AbstractList 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.
Author Chai, Xiaolin
Li, Zesong
Hao, Steven
Wu, Jian
Wang, Yunlong
Xiao, Pengchuan
Shao, Zhenlei
Sun, Kai
Zhang, Zishuo
Jiao, Judy
Yang, Diange
Jiang, Kun
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  organization: Hesai Technology Co., Ltd.,China
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  surname: Jiang
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  organization: Tsinghua University,State Key Laboratory of Automotive Safety and Energy, Center for Intelligent Connected Vehicles and Transportation, School of Vehicle and Mobility,China
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SubjectTerms Data collection
Laser radar
Measurement
Object detection
Semantics
Three-dimensional displays
Training
Title PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving
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