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SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
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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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Designing and Building an Intelligent Pavement Management System for Urban Road Networks
Moradi, M., & Assaf, G. J. (2023) · Sustainability · doi:10.3390/su15021157 · Cited by 33
3D point cloud descriptors: state-of-the-art
Han, X.-F., Feng, Z.-A., Sun, S., & Xiao, G. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10486-4 · Cited by 33
Bultmann, S., Quenzel, J., & Behnke, S. (2022) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2022.104286 · Cited by 34
Self-citationMultiscale Latent-Guided Entropy Model for LiDAR Point Cloud Compression
Fan, T., Gao, L., Xu, Y., Wang, D., & Zhu Li, B. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3276788 · Cited by 32
DUFOMap: Efficient Dynamic Awareness Mapping
Duberg, D., Zhang, Q., Jia, M., & Jensfelt, P. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3387658 · Cited by 32
LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models
Nakashima, K., & Kurazume, R. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611480 · Cited by 44
3D Vehicle Detection With RSU LiDAR for Autonomous Mine
Wang, G., Wu, J., Xu, T., & Tian, B. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3048985 · Cited by 32
Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning
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CarlaScenes: A synthetic dataset for odometry in autonomous driving
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BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR
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Open-world Semantic Segmentation for LIDAR Point Clouds
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LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDAR
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Scalable SoftGroup for 3D Instance Segmentation on Point Clouds
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Detailed Analysis on Generating the Range Image for LiDAR Point Cloud Processing
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DALES Objects: A Large Scale Benchmark Dataset for Instance Segmentation in Aerial Lidar
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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
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.
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