PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving

By Pengchuan Xiao, Zhenlei Shao, Steven Hao, Zishuo Zhang, Xiaolin Chai, Judy Jiao, Zesong Li, Jian Wu, Kai Sun, Kun Jiang and 2 more. International Conference on Intelligent Transportation Systems, 2021

320 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4b9c7196f5795681fe274f6c7fcb239a9aa3af94

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Citations in the corpus

UniSim: A Neural Closed-Loop Sensor Simulator (2023, methodology): “Dataset: We evaluate our method on the publicly available PandaSet [89] which contains 103 driving scenes captured in urban areas in San Francisco.”
Metric3D v2: A Versatile Monocular Geometric Foundation Model for Zero-Shot Metric Depth and Surface Normal Estimation (2024, unknown): “12, including comic-style (Row 2) and CG(computer graphics)-generated objects (Row5)”
Depth Anything 3: Recovering the Visual Space from Any Views (2025, unknown): “We trained our metric depth model on 14 datasets, including Taskonomy [120], DIML (Outdoor) [15], DDAD [30], Argoverse [101], Lyft [], PandaSet [105], Waymo [84], ScanNet++ [117], ARK-itScenes [5], Map-free [3], DSEC [24], Driving Stereo [109] and Cityscapes [16] datasets.”
Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image (2023, methodology): “Training Data DDAD [19] Outdoor LiDar ∼80K 36+ Lyft [24] Outdoor LiDar ∼50K 6+ Driving Stereo (DS) [62] Outdoor Stereo† ∼181K 1 DIML [9] Outdoor Stereo† ∼122K 10 Arogoverse2 [55] Outdoor LiDar ∼3515K 6+ Cityscapes [10] Outdoor Stereo† ∼170K 1 DSEC [16] Outdoor LiDar ∼26K 1 Mapillary PSD [34] Outdoor”
3D Object Detection for Autonomous Driving: A Comprehensive Survey (2022, background): “Some datasets (Liao et al., 2021; Xiao et al., 2021; Geyer et al., 2020; Wilson et al., 2021; Caesar et al., 2020) can provide more fine-grained object classes, including animals, barriers, traffic cones, etc.”