Cited by 2,294Advanced Neural Network ApplicationsAutonomous Vehicle Technology and Safety3D Shape Modeling and Analysis

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SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

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Behley, Jens, Garbade, Martin, Milioto, Andres, Quenzel, Jan, Behnke, Sven, Stachniss, Cyrill, Gall, Jurgen·2019·2019 IEEE/CVF International Conference on Computer Vision (ICCV)

DOI 10.1109/iccv.2019.00939

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Abstract

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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Authors: Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., & Gall, J.
Affiliations: University of Bonn (DE)
ORCID: Behley, Jens
Milioto, Andres
Quenzel, Jan
Behnke, Sven
Stachniss, Cyrill
Venue: 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher: IEEE
Published: 2019-10
Pages: 9296-9306
DOI: 10.1109/iccv.2019.00939
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

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