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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SemanticKITTI focuses on semantic segmentation of LiDAR point clouds and provides an important benchmark for sequence-level semantic scene completion.
“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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Argoverse 2: Next Generation Datasets for Self-Driving Perception and ForecastingPreprint
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Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion
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Capturing, Reconstructing, and Simulating: The UrbanScene3D Dataset
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- 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 · 2018: 1
- 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 › Engineering › Computational Mechanics › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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