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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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Mx2M: Masked Cross-Modality Modeling in Domain Adaptation for 3D Semantic Segmentation
Zhang, B., Wang, Z., Ling, Y., Guan, Y., Zhang, S., & Li, W. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25448 · Cited by 15
COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasets
Sanchez, J., Deschaud, J., & Goulette, F. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160539 · Cited by 13
Liu, J., Wu, Y., Gong, M., Miao, Q., Ma, W., & Xu, C. X. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612224 · Cited by 13
DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds
Peng, C., Wang, G., Lo, X. W., Wu, X., Xu, C., Tomizuka, M., Zhan, W., & Wang, H. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01550 · Cited by 13
4D Panoptic Segmentation as Invariant and Equivariant Field Prediction
Zhu, M., Han, S., Ghaffari, M., Cai, H., Porikli, F., & Borse, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02055 · Cited by 13
Hu, X., Xiong, G., Zang, Z., Jia, P., Han, Y., & Ma, J. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3366657 · Cited by 13
FLIC: Fast Lidar Image Clustering
Hasecke, F., Hahn, L., & Kummert, A. (2021) · International Conference on Pattern Recognition Applications and Methods · doi:10.5220/0010193700250035 · Cited by 13
Yang, C.-K., Chuang, Y., & Lin, Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00724 · Cited by 13
A New Framework for Registration of Semantic Point Clouds from Stereo and RGB-D Cameras
Zhang, R., Lin, T.-Y., Lin, C. E., Parkison, S. A., Clark, W., Grizzle, J. W., Eustice, R. M., & Ghaffari, M. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561929 · Cited by 13
Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs
Wang, H., Wang, Z., Lin, L., Xu, F., Yu, J., & Liang, H. (2021) · Remote Sensing · doi:10.3390/rs13204123 · Cited by 13
Yang, K., Bi, S., & Dong, M. (2020) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme46284.2020.9102769 · Cited by 13
Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class DistributionsPreprint
Alonso, Í., Riazuelo, L., Montesano, L., & Murillo, A. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12239 · Cited by 13
Generalized LOAM: LiDAR Odometry Estimation With Trainable Local Geometric Features
Honda, K., Koide, K., Yokozuka, M., Oishi, S., & Banno, A. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3219022 · Cited by 12
A Specialized Database for Autonomous Vehicles Based on the KITTI Vision Benchmark
Ortega-Gomez, J. I., Morales-Hernandez, L. A., & Cruz-Albarrán, I. A. (2023) · Electronics · doi:10.3390/electronics12143165 · Cited by 12
Large-Scale Spatio-Temporal Attention Based Entropy Model for Point Cloud Compression
Song, R. Y., Fu, C., Liu, S., & Li, G. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00343 · Cited by 12
City-scale continual neural semantic mapping with three-layer sampling and panoptic representation
Shi, Y., Yang, R., Wu, Z., Li, P., Liu, C., Zhao, H., & Zhou, G. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111145 · Cited by 12
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.
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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