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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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Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric SpacePreprint
Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02413 · Cited by 10,099
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles, R., Su, H., Mo, K., & Guibas, L. J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.16 · Cited by 10,029
The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139
Indoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33715-4_54 · Cited by 5,760
Research works printed but not linked: 2
- hua, 2018, Point-wise Convolutional Neural Networks, Proc of the IEEE Conf on Computer Vision and Pattern Recognition (CVPR)
- 10.1177/0278364918767506 · DOI
Cited by
Sorted by citations
Items 401–500 of 1,000
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Dong, H., Chen, X., & Stachniss, C. (2021) · European Conference on Mobile Robots · doi:10.1109/ecmr50962.2021.9568850 · Cited by 31
Self-citationLiMoSeg: Real-time Bird’s Eye View based LiDAR Motion Segmentation
Mohapatra, S., Hodaei, M., Yogamani, S. K., Milz, S., Gotzig, H., Simón, M., Rashed, H., & Maeder, P. (2022) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0010866000003124 · Cited by 31
Zhao, Y., Ma, X., Hu, B., Zhang, Q., Ye, M., & Zhou, G. (2023) · Computers & Graphics · doi:10.1016/j.cag.2023.01.011 · Cited by 31
Fuzzy Positive Learning for Semi-Supervised Semantic Segmentation
Qiao, P., Wei, Z., Wang, Y., Wang, Z., Song, G., Xu, F., Ji, X., Liu, C., & Chen, J. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01484 · Cited by 33
Towards intelligent ground filtering of large-scale topographic point clouds: A comprehensive survey
Qin, N., Tan, W., Guan, H., Wang, L., Ma, L., Tao, P., Fatholahi, S. N., Hu, X., & Li, J. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103566 · Cited by 31
Shu, J., Wang, S., Yu, S., & Zhang, J. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.045818 · Cited by 30
Prabhu, A., Liu, X., Spasojevic, I., Wu, Y., Shao, Y., Ong, D., Lei, J., Green, P. C., Chaudhari, P., & Kumar, V. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.111050 · Cited by 30
Frozen CLIP Transformer Is an Efficient Point Cloud Encoder
Huang, X., Huang, Z., Li, S., Qu, W., He, T., Hou, Y., Zuo, Y., & Ouyang, W. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.28013 · Cited by 30
Not All Voxels are Equal: Hardness-Aware Semantic Scene Completion with Self-Distillation
Wang, S., Yu, J., Li, W., Liu, W., Liu, X., Chen, J., & Zhu, J. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01401 · Cited by 30
Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion
Nunes, L., Marcuzzi, R., Mersch, B., Behley, J., & Stachniss, C. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01399 · Cited by 39
Self-citationLSK3DNet: Towards Effective and Efficient 3D Perception with Large Sparse Kernels
Feng, T., Wang, W., Ma, F., & Yang, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01413 · Cited by 33
Vinayaraj, P., Wang, W., & Nakamura, R. (2019) · Remote Sensing · doi:10.3390/rs11242961 · Cited by 30
Vanian, V., Ζαμανάκος, Γ., & Pratikakis, I. (2022) · Computers & Graphics · doi:10.1016/j.cag.2022.06.010 · Cited by 30
Static map generation from 3D LiDAR point clouds exploiting ground segmentation
Arora, M., Wiesmann, L., Chen, X., & Stachniss, C. (2022) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2022.104287 · Cited by 30
Self-citationGIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation
Saltori, C., Krivosheev, E., Lathuilière, S., Sebe, N., Galasso, F., Fiameni, G., Ricci, E., & Poiesi, F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19827-4_33 · Cited by 33
Efficient Point Cloud Segmentation with Geometry-Aware Sparse Networks
Ye, M., Wan, R., Xu, S., Cao, T., & Chen, Q. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19842-7_12 · Cited by 30
Ni, P., Li, X., Kong, D., & Yin, X. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3274949 · Cited by 30
FSS-Net: A Fast Search Structure for 3D Point Clouds in Deep Learning
Wang, J., Zhuang, Y., & Liu, Y. (2023) · International Journal of Network Dynamics and Intelligence · doi:10.53941/ijndi.2023.100005 · Cited by 30
See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data
Lu, Y., Jiang, Q., Chen, R., Hou, Y., Zhu, X., & Ma, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01981 · Cited by 29
Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning
Sun, T., Zhang, Z., Tan, X., Qu, Y., & Xie, Y. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3372449 · Cited by 29
Li, Y., Huang, Y., & Tao, Q. (2024) · Scientific Reports · doi:10.1038/s41598-024-68115-1 · Cited by 29
Deep learning for three-dimensional (3D) plant phenomics
Jin, S.-C., Li, D.-W., Yun, T., Tang, J., Wang, K., Li, S., Yang, H.-Y., Yang, S., Xu, S., Cao, L., et al. (2025) · Plant Phenomics · doi:10.1016/j.plaphe.2025.100107 · Cited by 30
Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection
Unal, O., Gool, L. V., & Dai, D. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00299 · Cited by 29
A Semantic Classification Approach for Indoor Robot Navigation
Alenzi, Z., Alenzi, E., Alqasir, M., Alruwaili, M., Alhmiedat, T. A., & Alia, O. M. (2022) · Electronics · doi:10.3390/electronics11132063 · Cited by 29
PanopticDepth: A Unified Framework for Depth-aware Panoptic Segmentation
Gao, N., He, F., Jia, J., Shan, Y., Zhang, H., Zhao, X., & Huang, K. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00168 · Cited by 32
Segregator: Global Point Cloud Registration with Semantic and Geometric Cues
Yin, P., Yuan, S., Cao, H., Ji, X., Zhang, S., & Xie, L. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160798 · Cited by 29
MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Clouds
Liu, J., Chang, C., Liu, J., Wu, X., Ma, L., & Qi, X. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00904 · Cited by 29
Kim, J., Kim, J., Kim, Y., & Kim, H. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105108 · Cited by 29
DepthSSC: Monocular 3D Semantic Scene Completion via Depth-Spatial Alignment and Voxel Adaptation
Yao, J., Zhang, J., Pan, X., Wu, T., & Xiao, C. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00216 · Cited by 29
Domain Adaptive LiDAR Point Cloud Segmentation With 3D Spatial Consistency
Xiao, A., Guan, D., Zhang, X., & Lu, S. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3335879 · Cited by 28
Hydra-Multi: Collaborative Online Construction of 3D Scene Graphs with Multi-Robot Teams
Chang, Y., Hughes, N., Ray, A., & Carlone, L. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341838 · Cited by 28
Overview of image-based 3D reconstruction technology
Niu, Y., Liu, L., Huang, F., Huang, S., & Chen, S. (2024) · Journal of the European Optical Society Rapid Publications · doi:10.1051/jeos/2024018 · Cited by 33
Chen, Z., Lu, Y., Long, S., Campello, V. M., Bai, J., & Lekadir, K. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3399762 · Cited by 28
Rochan, M., Aich, S., Corral-Soto, E. R., Nabatchian, A., & Liu, B. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811654 · Cited by 28
SemAlign: Annotation-Free Camera-LiDAR Calibration with Semantic Alignment Loss
Liu, Z., Tang, H., Zhu, S., & Han, S. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9635964 · Cited by 28
LiDAR-based Drivable Region Detection for Autonomous Driving
Xue, H., Fu, H., Ren, R., Zhang, J., Liu, B., Fan, Y., & Dai, B. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636289 · Cited by 28
Semantic scan context: a novel semantic-based loop-closure method for LiDAR SLAM
Li, L., Kong, X., Zhao, X., Huang, T., & Liu, Y. (2022) · Autonomous Robots · doi:10.1007/s10514-022-10037-w · Cited by 28
LUMPI: The Leibniz University Multi-Perspective Intersection Dataset
Busch, S., Koetsier, C., Axmann, J., & Claus Brenner, A. (2022) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv51971.2022.9827157 · Cited by 28
Nonparametric Background Model-Based LiDAR SLAM in Highly Dynamic Urban Environments
Park, J., Cho, Y., & Shin, Y.-S. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3204917 · Cited by 30
SectionKey: 3-D Semantic Point Cloud Descriptor for Place Recognition
Jin, S., Wu, Z., Zhao, C., Zhang, J., Peng, G., & Wang, D. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981605 · Cited by 28
Wang, Z., Geng, Y., Jia, L., Qin, Y., Chai, Y., Tong, L., & Liu, K. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3281352 · Cited by 28
PCGen: Point Cloud Generator for LiDAR Simulation
Li, C., Ren, Y., & Liu, B. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161226 · Cited by 28
Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent VehiclesPreprint
Tian, Y., Carballo, A., Li, R., & Takeda, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.13588 · Cited by 34
Zhu, Y., Miao, C., Xue, H., Li, Z., Yu, Y., Xu, W., Su, L., & Qiao, C. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3616661 · Cited by 27
Variational Distillation for Multi-View Learning
Tian, X., Zhang, Z., Wang, C., Zhang, W., Qu, Y., Ma, L., Wu, Z., Xie, Y., & Tao, D. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3343717 · Cited by 27
Unified 3D and 4D Panoptic Segmentation via Dynamic Shifting Networks
Hong, F., Kong, L., Zhou, H., Zhu, X., Li, H., & Liu, Z. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3349304 · Cited by 27
Yang, L., Lin, Y.-C., Cai, H., & Habib, A. F. (2024) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-5640 · Cited by 28
Multi-Context Point Cloud Dataset and Machine Learning for Railway Semantic Segmentation
Kharroubi, A., Ballouch, Z., Hajji, R., Yarroudh, A., & Billen, R. (2024) · Infrastructures · doi:10.3390/infrastructures9040071 · Cited by 27
Li, M., Lin, S., Wang, Z., Shen, Y., Zhang, B., & Ma, L. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110701 · Cited by 31
Pan, Y., Wang, M., Lu, L., Wei, R., Cavazzi, S., Peck, M., & Brilakis, I. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105654 · Cited by 27
WHU-Railway3D: A Diverse Dataset and Benchmark for Railway Point Cloud Semantic Segmentation
Qiu, B., Zhou, Y., Dai, L., Wang, B., Li, J., Dong, Z., Wen, C., Ma, Z., & Yang, B. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3469546 · Cited by 27
OccGen: Generative Multi-modal 3D Occupancy Prediction for Autonomous Driving
Wang, G., Wang, Z., Pin, T., Zheng, J., Ren, X., Feng, B., & Ma, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72661-3_6 · Cited by 27
M2CS: A Multimodal and Campus‐Scapes Dataset for Dynamic SLAM and Moving Object Perception
Zhao, H., Yao, M., Zhao, Y., Jiang, Y., Zhang, H., Xiao, X., & Gao, K. (2024) · Journal of Field Robotics · doi:10.1002/rob.22468 · Cited by 35
SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud
Wang, N., Guo, R., Shi, C., Wang, Z., Zhang, H., Lu, H., Zheng, Z., & Chen, X. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3568001 · Cited by 27
Sánchez-Montero, M., Morales, J., Martínez, J. L., Fernández-Lozano, J. J., & Garcia-Cerezo, A. J. (2022) · Sensors · doi:10.3390/s22155599 · Cited by 27
Zhong, J.-X., Zhou, K., Hu, Q., Wang, B., Trigoni, N., & Markham, A. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00832 · Cited by 27
Yuan, Z., Cheng, M., Zeng, W., Su, Y., Liu, W., Yu, S., & Wang, C. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3234542 · Cited by 27
Kreuzberg, L., Zulfikar, I. E., Mahadevan, S., Engelmann, F., & Leibe, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25056-9_34 · Cited by 27
Zhang, Q., Hou, J., & Qian, Y. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3286981 · Cited by 27
ORORA: Outlier-Robust Radar Odometry
Lim, H., Han, K., Shin, G., Kim, G., Hong, S., & Myung, H. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160997 · Cited by 27
ACL-SPC: Adaptive Closed-Loop System for Self-Supervised Point Cloud Completion
Hong, S., Yavartanoo, M., Neshatavar, R., & Lee, K. M. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00910 · Cited by 27
Zhou, B., Xie, J., Pan, Y., Wu, J., & Lu, C. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3325687 · Cited by 27
SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion
Mei, J., Yang, Y., Wang, M., Huang, T., Yang, X., & Liu, Y. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341742 · Cited by 27
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Cao, H., Xu, Y., Yang, J., Yin, P., Yuan, S., & Xie, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01724 · Cited by 27
Unsupervised Visual Representation Learning via Dual-Level Progressive Similar Instance Selection
Fan, H., Liu, P., Xu, M., & Yang, Y. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3054978 · Cited by 27
MFFNet: multimodal feature fusion network for point cloud semantic segmentation
Ren, D., Li, J., Wu, Z., Guo, J. J., Wei, M., & Guo, Y. (2023) · The Visual Computer · doi:10.1007/s00371-023-02907-w · Cited by 26
Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scenes
Yoo, S., Jeong, Y., Jameela, M., & Sohn, G. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00699 · Cited by 26
Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation
Saltori, C., Galasso, F., Fiameni, G., Sebe, N., Poiesi, F., & Ricci, E. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3310261 · Cited by 26
Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper
Vinodkumar, P. K., Karabulut, D., Avots, E., Ozcinar, C., & Anbarjafari, G. (2024) · Entropy · doi:10.3390/e26030235 · Cited by 30
LiDAR-Net: A Real-Scanned 3D Point Cloud Dataset for Indoor Scenes
Guo, Y., Li, Y., Ren, D., Zhang, X., Li, J., Pu, L., Ma, C., Zhan, X., Guo, J., Wei, M., et al. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02076 · Cited by 26
PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness
Cao, A.-Q., Dai, A., & de Charette, R. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01379 · Cited by 26
Multi-Space Alignments Towards Universal LiDAR Segmentation
Liu, Y., Kong, L., Wu, X., Chen, R., Li, X. Y., Pan, L., Liu, Z., & Ma, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01388 · Cited by 26
PBP-Net: Point Projection and Back-Projection Network for 3D Point Cloud Segmentation
Yang, J., Lee, C.-H., Ahn, P., Lee, H., Yi, E., & Kim, J. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341776 · Cited by 26
Continuous Mapping Convolution for Large-Scale Point Clouds Semantic Segmentation
Yan, K., Hu, Q., Wang, H., Huang, X., Li, L., & Ji, S. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3107006 · Cited by 26
A fast LiDAR place recognition and localization method by fusing local and global search
Shi, P., Li, J., & Zhang, Y. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.07.008 · Cited by 26
LUCOOP: Leibniz University Cooperative Perception and Urban Navigation Dataset
Axmann, J., Moftizadeh, R., Su, J., Tennstedt, B., Zou, Q., Yuan, Y., Ernst, D., Alkhatib, H., Claus Brenner, A., & Schön, S. (2023) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55152.2023.10186693 · Cited by 28
An Efficient Accelerator for Point-based and Voxel-based Point Cloud Neural Networks
Yang, X., Fu, T., Dai, G., Zeng, S., Zhong, K., Hong, K., & Wang, Y. (2023) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac56929.2023.10247806 · Cited by 26
Cen, J., Zhang, S., Pei, Y., Li, K., Zheng, H., Luo, M., Zhang, Y., & Chen, Q. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3335771 · Cited by 26
S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point CloudsPreprint
Cheng, R., Agia, C., Ren, Y., Li, X., & Bingbing, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.09242 · Cited by 26
Three Pillars Improving Vision Foundation Model Distillation for Lidar
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Cho, S.-J., Kim, C., Sunwoo, M., & Jo, K. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3035801 · Cited by 25
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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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