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Cited by 680Advanced Neural Network ApplicationsAutonomous Vehicle Technology and SafetyAdvanced Vision and Imaging

The ApolloScape Dataset for Autonomous Driving

Open access
Huang, Xinyu, Cheng, Xinjing, Geng, Qichuan, Cao, Binbin, Zhou, Dingfu, Wang, Peng, Lin, Yuanqing, Yang, Ruigang·2018·2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
DOI 10.1109/cvprw.2018.00141
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Abstract

Scene parsing aims to assign a class (semantic) label for each pixel in an image. It is a comprehensive analysis of an image. Given the rise of autonomous driving, pixel-accurate environmental perception is expected to be a key enabling technical piece. However, providing a large scale dataset for the design and evaluation of scene parsing algorithms, in particular for outdoor scenes, has been difficult. The per-pixel labelling process is prohibitively expensive, limiting the scale of existing ones. In this paper, we present a large-scale open dataset, ApolloScape, that consists of RGB videos and corresponding dense 3D point clouds. Comparing with existing datasets, our dataset has the following unique properties. The first is its scale, our initial release contains over 140K images - each with its per-pixel semantic mask, up to 1M is scheduled. The second is its complexity. Captured in various traffic conditions, the number of moving objects averages from tens to over one hundred (Figure 1). And the third is the 3D attribute, each image is tagged with high-accuracy pose information at cm accuracy and the static background point cloud has mm relative accuracy. We are able to label these many images by an interactive and efficient labelling pipeline that utilizes the high-quality 3D point cloud. Moreover, our dataset also contains different lane markings based on the lane colors and styles. We expect our new dataset can deeply benefit various autonomous driving related applications that include but not limited to 2D/3D scene understanding, localization, transfer learning, and driving simulation.

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  • Task-specific splits are provided because class imbalance may be an issue depending on the task.

    “Depending on the particular task (see Section 4, for the full list), the class imbalance may be an issue [19], therefore, task-specific splits are also provided.”

    From WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019) · cites this paper as [19]

    Section 5.1 (p. 12) identifies a class-imbalance problem: traffic lights occur less often than light poles, and the authors suggest better class-balancing strategies. Section 5.2 (p. 12) describes separate task benchmarks and dataset splits, but does not say those splits were provided because of class imbalance. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Most reviewed datasets provide ground-truth labels for 2D object detection and semantic segmentation tasks.

    “Most of the reviewed datasets provide ground-truth labels for 2D object detection and semantic segmentation tasks [60], [75], [88], [90]-[93].”

    From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [60]

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Research works printed but not linked: 20

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Authors
Huang, X., Cheng, X., Geng, Q., Cao, B., Zhou, D., Wang, P., Lin, Y., & Yang, R.
Affiliations
National Engineering Laboratory of Deep Learning Technology and Application (CN)
ORCID
Venue
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Publisher
IEEE
Published
2018-06
Pages
1067-10676
DOI
10.1109/cvprw.2018.00141
OpenAlex
W2791763440
Cited by
679
References
66
Citations by year
2026: 44 · 2025: 100 · 2024: 99 · 2023: 95 · 2022: 69 · 2021: 85 · 2020: 89 · 2019: 80 · 2018: 17 · 2017: 1
FWCI
20.42
Type
conference paper
Language
en
Open access
Locations
  • IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • TIB Data Manager
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
Keywords
autonomous driving, scene parsing, semantic segmentation, 3D point clouds, outdoor scenes, camera pose estimation, lane markings, driving simulation, environmental perception

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