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SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Open accessAbstract
Semantic scene understanding is important for various applications. In particular, self-driving cars need a fine-grained understanding of the surfaces and objects in their vicinity. Light detection and ranging (LiDAR) provides precise geometric information about the environment and is thus a part of the sensor suites of almost all self-driving cars. Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR. In this paper, we introduce a large dataset to propel research on laser-based semantic segmentation. We annotated all sequences of the KITTI Vision Odometry Benchmark and provide dense point-wise annotations for the complete 360-degree field-of-view of the employed automotive LiDAR. We propose three benchmark tasks based on this dataset: (i) semantic segmentation of point clouds using a single scan, (ii) semantic segmentation using multiple past scans, and (iii) semantic scene completion, which requires to anticipate the semantic scene in the future. We provide baseline experiments and show that there is a need for more sophisticated models to efficiently tackle these tasks. Our dataset opens the door for the development of more advanced methods, but also provides plentiful data to investigate new research directions.
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“Additionally, SemanticKITTI [24] focuses on semantic segmentation of LiDAR point clouds and provides an important benchmark for sequence-level semantic scene completion.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [24]
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Self-citation
Metadata
- Authors
- Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., & Gall, J.
- Affiliations
- University of Bonn (DE)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 9296-9306
- OpenAlex
- W2965803762
- Cited by
- 2,294
- References
- 77
- Citations by year
- 2026: 249 · 2025: 457 · 2024: 520 · 2023: 423 · 2022: 286 · 2021: 236 · 2020: 110 · 2019: 11
- FWCI
- 52.41
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- TIB Data Manager
- Funders
- Deutsche Forschungsgemeinschaft (EXC-2070, FOR 1505, BE 5996/1-1, GA 1927/2-2, 390732324)
- 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 Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Keywords
- semantic scene understanding, LiDAR, point cloud semantic segmentation, automotive LiDAR, SemanticKITTI, point cloud annotation, semantic scene completion, autonomous driving, benchmark dataset
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