Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods
Abstract
3D semantic segmentation is a fundamental task for robotic and autonomous driving applications. Recent works have been focused on using deep learning techniques, whereas developing fine-annotated 3D LiDAR datasets is extremely labor intensive and requires professional skills. The performance limitation caused by insufficient datasets is called data hunger problem. This research provides a comprehensive survey on the question: are we hungry for 3D LiDAR data for semantic segmentation? The studies are conducted at three levels. First, a broad review to the main 3D LiDAR datasets is conducted, followed by a statistical analysis on three representative datasets to gain an in-depth view on the datasets’ size, diversity and quality, which are the critical factors in learning deep models. Second, an organized survey of 3D semantic segmentation methods is given with a focus on the mainstream of the latest research trend using deep learning techniques, followed by a systematic survey to the existing efforts to solve the data hunger problem. Finally, an insightful discussion of the remaining problems on both methodological and datasets’ viewpoints, and the open questions on dataset bias, domain and semantic gap are given, leading to potential topics in future works. To the best of our knowledge, this is the first work to study the data hunger problem for 3D semantic segmentation using deep learning techniques, which are addressed in both methodological and dataset review, and we share findings and discussions through a comprehensive dataset analysis.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
LiDAR pointclouds are relatively sparse and lack color information, which results in a significant classification error in fine-grained segmentation.
“However, the pointclouds from these sensors are relatively sparse and lack color information, which results in a significant classification error in fine-grained segmentation [42].”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [42]
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
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 · see citing works
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
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 · see citing works
A survey on Image Data Augmentation for Deep Learning
Shorten, C., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0197-0 · Cited by 13,203 · see citing works
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 · see citing works
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 9,834 · see citing works
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 9,767 · see citing works
Domain-Adversarial Training of Neural Networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_10 · Cited by 8,318 · see citing works
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945 · see citing works
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152 · see citing works
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 · see citing works
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016) · arXiv · doi:10.48550/arxiv.1602.07360 · Cited by 6,201 · see citing works
Prototypical Networks for Few-shot LearningPreprint
Snell, J., Swersky, K., & Zemel, R. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.05175 · Cited by 5,863 · see citing works
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 · see citing works
Context Encoders: Feature Learning by Inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691 · see citing works
3D is here: Point Cloud Library (PCL)
Rusu, R., & Cousins, S. (2011) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5980567 · Cited by 5,513 · see citing works
Learning Transferable Features with Deep Adaptation Networks
Long, M., Cao, Y., Wang, J., & Jordan, M. I. (2015) · arXiv · doi:10.48550/arxiv.1502.02791 · Cited by 4,505 · see citing works
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
Dai, A., Chang, A. X., Savva, M., Halber, M., Funkhouser, T., & Niessner, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.261 · Cited by 4,368 · see citing works
Tarvainen, A., & Valpola, H. (2017) · arXiv · doi:10.48550/arxiv.1703.01780 · Cited by 4,275 · see citing works
Zhang, R., Isola, P., & Efros, A. A. (2016) · European Conference on Computer Vision · doi:10.1007/978-3-319-46487-9_40 · Cited by 3,803 · see citing works
CARLA: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017) · arXiv · doi:10.48550/arxiv.1711.03938 · Cited by 3,718 · see citing works
Multi-view 3D Object Detection Network for Autonomous Driving
Chen, X., Ma, H., Wan, J., Li, B., & Xia, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.691 · Cited by 3,450 · see citing works
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., et al. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00252 · Cited by 3,437 · see citing works
KPConv: Flexible and Deformable Convolution for Point Clouds
Thomas, H., Qi, C. R., Deschaud, J., Marcotegui, B., Goulette, F., & Guibas, L. J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00651 · Cited by 3,400 · see citing works
LOAM: Lidar Odometry and Mapping in Real-time
Zhang, J., & Singh, S. (2014) · Robotics: Science and Systems X · doi:10.15607/rss.2014.x.007 · Cited by 3,303 · see citing works
A Survey on Deep Transfer Learning
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01424-7_27 · Cited by 3,165 · see citing works
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.222 · Cited by 3,150 · see citing works
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
Noroozi, M., & Favaro, P. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_5 · Cited by 2,775 · see citing works
Unsupervised Visual Representation Learning by Context Prediction
Doersch, C., Gupta, A., & Efros, A. A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.167 · Cited by 2,629 · see citing works
A survey on deep learning techniques for image and video semantic segmentation
García-García, A., Orts‐Escolano, S., Oprea, S., Villena-Martínez, V., Martínez-González, P., & García‐Rodríguez, J. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.05.018 · Cited by 2,489 · see citing works
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Sun, C., Shrivastava, A., Singh, S., & Gupta, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.97 · Cited by 2,484 · see citing works
Deep Learning for 3D Point Clouds: A Survey
Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L., & Bennamoun, M. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3005434 · Cited by 2,445 · see citing works
Torralba, A., & Efros, A. A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995347 · Cited by 2,440 · see citing works
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., & Lopez, A. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.352 · Cited by 2,338 · see citing works
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., & Gall, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00939 · Cited by 2,294 · see citing works
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., & Markham, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01112 · Cited by 2,275 · see citing works
Stanley: The robot that won the DARPA Grand Challenge
Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoffmann, G., et al. (2006) · Journal of Field Robotics · doi:10.1002/rob.20147 · Cited by 2,237 · see citing works
PointConv: Deep Convolutional Networks on 3D Point Clouds
Wu, W., Qi, Z., & Li, F. (2019) · 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.2019.00985 · Cited by 1,972 · see citing works
Real-time loop closure in 2D LIDAR SLAM
Hess, W., Kohler, D., Rapp, H., & Andor-Ardó, D. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487258 · Cited by 1,945 · see citing works
Playing for Data: Ground Truth from Computer Games
Richter, S. R., Vineet, V., Roth, S., & Koltun, V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46475-6_7 · Cited by 1,928 · see citing works
3D Semantic Parsing of Large-Scale Indoor Spaces
Armeni, I., Şener, O., Zamir, A., Jiang, H., Brilakis, I., Fischer, M. A., & Savarese, S. (2016) · 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.2016.170 · Cited by 1,897 · see citing works
Boosting for transfer learning
Dai, W., Yang, Q., Xue, G.-R., & Yu, Y. H. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273521 · Cited by 1,788 · see citing works
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Graham, B., Engelcke, M., & Maaten, L. V. D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00961 · Cited by 1,788 · see citing works
Kirillov, A., He, K., Girshick, R., Rother, C., & Dollár, P. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00963 · Cited by 1,659 · see citing works
Scheirer, W. J., de Rezende Rocha, A., Sapkota, A., & Boult, T. E. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.256 · Cited by 1,573 · see citing works
Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs
Landrieu, L., & Simonovsky, M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00479 · Cited by 1,543 · see citing works
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
Vu, T.-H., Jain, H., Bucher, M., Cord, M., & Perez, P. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00262 · Cited by 1,524 · see citing works
Argoverse: 3D Tracking and Forecasting With Rich Maps
Chang, M.-F., Lambert, J., Sangkloy, P., Singh, J., Bak, S., Hartnett, A., Wang, D., Carr, P., Lucey, S., Ramanan, D., et al. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00895 · Cited by 1,474 · see citing works
Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training
Zou, Y., Yu, Z., Vijaya Kumar, B. V. K., & Wang, J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_18 · Cited by 1,464 · see citing works
Feng, D., Haase-Schutz, C., Rosenbaum, L., Hertlein, H., Glaser, C., Timm, F., Wiesbeck, W., & Dietmayer, K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2972974 · Cited by 1,425 · see citing works
RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation
Milioto, A., Vizzo, I., Behley, J., & Stachniss, C. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967762 · Cited by 1,308 · see citing works
Big data deep learning: Challenges and perspectives
Chen, X.-W., & Lin, X. (2014) · IEEE Access · doi:10.1109/access.2014.2325029 · Cited by 1,255 · see citing works
Domain-adversarial training of neural networks
GaninYaroslav, UstinovaEvgeniya, AjakanHana, GermainPascal, LarochelleHugo, LavioletteFrançois, MarchandMario, & LempitskyVictor (2016) · Journal of Machine Learning Research · doi:10.5555/2946645.2946704 · Cited by 1,203 · see citing works
ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation
Lin, D., Dai, J., Jia, J., He, K., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.344 · Cited by 1,129 · see citing works
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation
Dai, J., He, K., & Sun, J. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.191 · Cited by 1,118 · see citing works
A Review on Deep Learning Techniques Applied to Semantic SegmentationPreprint
García-García, A., Orts‐Escolano, S., Oprea, S., Villena-Martínez, V., & García‐Rodríguez, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.06857 · Cited by 1,049 · see citing works
Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation
Papandreou, G., Chen, L.-C., Murphy, K. P., & Yuille, A. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.203 · Cited by 1,041 · see citing works
SO-Net: Self-Organizing Network for Point Cloud Analysis
Li, J., Chen, B. M., & Lee, G. H. (2018) · 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.2018.00979 · Cited by 1,025 · see citing works
Unsupervised Learning of Visual Representations Using Videos
Wang, X., & Gupta, A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.320 · Cited by 1,005 · see citing works
What’s the Point: Semantic Segmentation with Point Supervision
Bearman, A., Russakovsky, O., Ferrari, V., & Fei-Fei, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_34 · Cited by 986 · see citing works
Wu, B., Wan, A., Yue, X., & Keutzer, K. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8462926 · Cited by 967 · see citing works
SEMANTIC3D.NET: A NEW LARGE-SCALE POINT CLOUD CLASSIFICATION BENCHMARK
Häckel, T., Savinov, N., Ladický, Ľ., Wegner, J. D., Schindler, K., & Pollefeys, M. (2017) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-1-w1-91-2017 · Cited by 859 · see citing works
Deep Learning: A Critical Appraisal
Marcus, G. (2018) · arXiv · doi:10.48550/arxiv.1801.00631 · Cited by 853 · see citing works
One-Shot Learning for Semantic Segmentation
Shaban, A., Bansal, S., Liu, Z., Essa, I. A., & Boots, B. (2017) · British Machine Vision Conference (BMVC) · doi:10.5244/c.31.167 · Cited by 851 · see citing works
SEGCloud: Semantic Segmentation of 3D Point Clouds
Tchapmi, L. P., Choy, C., Armeni, I., Gwak, J., & Savarese, S. (2017) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00067 · Cited by 824 · see citing works
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
Su, H., Jampani, V., Sun, D., Maji, S., Kalogerakis, E., Yang, M., & Kautz, J. (2018) · 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.2018.00268 · Cited by 804 · see citing works
Graph Attention Convolution for Point Cloud Semantic Segmentation
Wang, L., Huang, Y., Hou, Y., Zhang, S., & Shan, J. (2019) · 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.2019.01054 · Cited by 782 · see citing works
Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation
Kolesnikov, A., & Lampert, C. H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_42 · Cited by 773 · see citing works
Wu, B., Zhou, X., Zhao, S., Yue, X., & Keutzer, K. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793495 · Cited by 761 · see citing works
Martin Weinmann, A., Jutzi, B., Hinz, S., & Mallet, C. (2015) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.01.016 · Cited by 706 · see citing works
Zhang, C., Lin, G., Liu, F., Yao, R., & Shen, C. (2019) · 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.2019.00536 · Cited by 702 · see citing works
From image-level to pixel-level labeling with Convolutional Networks
Pinheiro, P. O., & Collobert, R. (2015) · 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.2015.7298780 · Cited by 691 · see citing works
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
Xie, S., Gu, J., Guo, D., Qi, C. R., Guibas, L. J., & Litany, O. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58580-8_34 · Cited by 655 · see citing works
Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather
Bijelic, M., Gruber, T., Mannan, F., Kraus, F., Ritter, W., Dietmayer, K., & Heide, F. (2020) · 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/cvpr42600.2020.01170 · Cited by 647 · see citing works
Constrained Convolutional Neural Networks for Weakly Supervised Segmentation
Pathak, D., Krähenbühl, P., & Darrell, T. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.209 · Cited by 629 · see citing works
Unity: A General Platform for Intelligent Agents
Juliani, A., Berges, V.-P., Teng, E., Cohen, A., Harper, J., Elion, C., Goy, C., Gao, Y., Henry, H., Mattar, M., et al. (2018) · arXiv · doi:10.48550/arxiv.1809.02627 · Cited by 626 · see citing works
The ApolloScape Open Dataset for Autonomous Driving and Its Application
Huang, X., Wang, P., Cheng, X., Zhou, D., Geng, Q., & Yang, R. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2926463 · Cited by 615 · see citing works
Tangent Convolutions for Dense Prediction in 3D
Tatarchenko, M., Park, J., Koltun, V., & Zhou, Q.-Y. (2018) · 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.2018.00409 · Cited by 592 · see citing works
Vehicle Detection from 3D Lidar Using Fully Convolutional Network
Li, B., Zhang, T., & Xia, T. (2016) · Robotics: Science and Systems XII · doi:10.15607/rss.2016.xii.042 · Cited by 577 · see citing works
Survey on semantic segmentation using deep learning techniques
Lateef, F., & Ruichek, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.02.003 · Cited by 561 · see citing works
Learning Features by Watching Objects Move
Pathak, D., Girshick, R. B., Dollár, P., Darrell, T., & Hariharan, B. (2016) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2017.638 · Cited by 549 · see citing works
Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation
Xie, Y., Tian, J., & Zhu, X. X. (2020) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2019.2937630 · Cited by 522 · see citing works
SalsaNext: Fast, Uncertainty-Aware Semantic Segmentation of LiDAR Point Clouds
Cortinhal, T., Tzelepis, G., & Aksoy, E. E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64559-5_16 · Cited by 513 · see citing works
Recurrent Slice Networks for 3D Segmentation of Point Clouds
Huang, Q., Wang, W., & Neumann, U. (2018) · 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.2018.00278 · Cited by 500 · see citing works
Undoing the Damage of Dataset Bias
Khosla, A., Zhou, T., Malisiewicz, T., Efros, A. A., & Torralba, A. B. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33718-5_12 · Cited by 482 · see citing works
Wang, Y.-X., Ramanan, D., & Hebert, M. (2017) · neural information processing systems · Cited by 468 · see citing works
Modeling Point Clouds With Self-Attention and Gumbel Subset Sampling
Yang, J., Zhang, Q., Ni, B., Li, L., Liu, J., Zhou, M., & Tian, Q. (2019) · 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.2019.00344 · Cited by 465 · see citing works
A REVIEW OF POINT CLOUDS SEGMENTATION AND CLASSIFICATION ALGORITHMS
Grilli, E., Menna, F., & Remondino, F. (2017) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-2-w3-339-2017 · Cited by 440 · see citing works
3D point cloud segmentation: A survey
Nguyen, A., & Le, B. H. (2013) · IEEE Conference on Robotics, Automation and Mechatronics (RAM) · doi:10.1109/ram.2013.6758588 · Cited by 437 · see citing works
SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation
Xu, C., Wu, B., Wang, Z., Zhan, W., Vajda, P., Keutzer, K., & Tomizuka, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58604-1_1 · Cited by 436 · see citing works
Richter, S. R., Hayder, Z., & Koltun, V. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.243 · Cited by 434 · see citing works
Shape-based recognition of 3D point clouds in urban environments
Golovinskiy, A., Kim, V. G., & Funkhouser, T. A. (2009) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459471 · Cited by 429 · see citing works
ShellNet: Efficient Point Cloud Convolutional Neural Networks Using Concentric Shells Statistics
Zhang, Z., Hua, B., & Yeung, S.-K. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00169 · Cited by 428 · see citing works
Review: Deep Learning on 3D Point Clouds
Bello, S. A., Yu, S., Wang, C., Adam, J. M., & Li, J. (2020) · Remote Sensing · doi:10.3390/rs12111729 · Cited by 399 · see citing works
Few-Shot Semantic Segmentation with Prototype Learning.
Dong, N., & Xing, E. P. (2018) · Cited by 383 · see citing works
FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY
Häckel, T., Wegner, J. D., & Schindler, K. (2016) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iii-3-177-2016 · Cited by 373 · see citing works
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data
Anguelov, D., Taskar, B., Chatalbashev, V., Koller, D., Gupta, D., Heitz, G., & Ng, A. Y. (2005) · 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.2005.133 · Cited by 360 · see citing works
Point cloud labeling using 3D Convolutional Neural Network
Huang, J., & You, S. O. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900038 · Cited by 353 · see citing works
Contextual classification with functional Max-Margin Markov Networks
Munoz, D., Bagnell, J. A., Vandapel, N., & Hebert, M. (2009) · 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.2009.5206590 · Cited by 346 · see citing works
Roynard, X., Deschaud, J., & Goulette, F. (2017) · HAL · Cited by 343 · see citing works
Deep Projective 3D Semantic Segmentation
Lawin, F. J., Danelljan, M., Tosteberg, P., Bhat, G., Khan, F. S., & Felsberg, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-64689-3_8 · Cited by 342 · see citing works
PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic SegmentationPreprint
Jiang, M., Wu, Y., Zhao, T., Zhao, Z., & Lu, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.00652 · Cited by 338 · see citing works
Deep Learning Advances in Computer Vision with 3D Data
Ioannidou, A., Chatzilari, E., Nikolopoulos, S., & Kompatsiaris, I. Y. (2017) · ACM Computing Surveys · doi:10.1145/3042064 · Cited by 324 · see citing works
VV-Net: Voxel VAE Net With Group Convolutions for Point Cloud Segmentation
Meng, H.-Y., Gao, L., Lai, Y., & Manocha, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00859 · Cited by 322 · see citing works
A-CNN: Annularly Convolutional Neural Networks on Point Clouds
Komarichev, A., Zhong, Z., & Hua, J. (2019) · 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.2019.00760 · Cited by 306 · see citing works
Boosting Self-Supervised Learning via Knowledge Transfer
Noroozi, M., Vinjimoor, A., Favaro, P., & Pirsiavash, H. (2018) · 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.2018.00975 · Cited by 298 · see citing works
Self-Supervised Pretraining of 3D Features on any Point-Cloud
Zhang, Z., Girdhar, R., Joulin, A., & Misra, I. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01009 · Cited by 285 · see citing works
Decoupled Deep Neural Network for Semi-supervised Semantic SegmentationPreprint
Hong, S., Noh, H., & Han, B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.04924 · Cited by 283 · see citing works
Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds
Engelmann, F., Kontogianni, T., Hermans, A., & Leibe, B. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.90 · Cited by 280 · see citing works
Methods and datasets on semantic segmentation: A review
Yu, H., Yang, Z., Tan, L., Wang, Y., Sun, W. J., Sun, M., & Tang, Y. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.03.037 · Cited by 279 · see citing works
Fully Convolutional Multi-Class Multiple Instance LearningPreprint
Pathak, D., Shelhamer, E., Long, J., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.7144 · Cited by 263 · see citing works
The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes
Patil, A., Malla, S., Gang, H., & Chen, Y.-T. (2019) · 2019 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2019.8793925 · Cited by 263 · see citing works
Predicting Semantic Map Representations From Images Using Pyramid Occupancy Networks
Roddick, T., & Cipolla, R. (2020) · 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/cvpr42600.2020.01115 · Cited by 263 · see citing works
Weakly Supervised Semantic Point Cloud Segmentation: Towards 10× Fewer Labels
Xu, X., & Lee, G. H. (2020) · 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/cvpr42600.2020.01372 · Cited by 258 · see citing works
Unstructured Point Cloud Semantic Labeling Using Deep Segmentation Networks
Boulch, A., Saux, B. L., & Audebert, N. (2017) · Eurographics · doi:10.2312/3dor.20171047 · Cited by 255 · see citing works
Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds
Engelmann, F., Kontogianni, T., Hermans, A., & Leibe, B. (2017) · RWTH Publications (RWTH Aachen) · Cited by 254 · see citing works
A2D2: Audi Autonomous Driving Dataset
Geyer, J., Kassahun, Y., Mahmudi, M., Ricou, X., Durgesh, R., Chung, A. S., Hauswald, L., Pham, V. H., Mühlegg, M., Dorn, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2004.06320 · Cited by 250 · see citing works
Tommasi, T., Patricia, N., Caputo, B., & Tuytelaars, T. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_2 · Cited by 245 · see citing works
Zhu, H., Meng, F., Cai, J., & Lu, S. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.10.012 · Cited by 233 · see citing works
Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation
Jiang, L., Zhao, H., Liu, S., Shen, X., Fu, C., & Jia, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01053 · Cited by 225 · see citing works
xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
Jaritz, M., Vu, T.-H., de Charette, R., Wirbel, E., & Perez, P. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01262 · Cited by 224 · see citing works
Contextually guided semantic labeling and search for three-dimensional point clouds
Anand, A., Koppula, H. S., Joachims, T., & Saxena, A. (2012) · The International Journal of Robotics Research · doi:10.1177/0278364912461538 · Cited by 216 · see citing works
Fully-Convolutional Point Networks for Large-Scale Point Clouds
Rethage, D., Wald, J., Sturm, J., Navab, N., & Tombari, F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01225-0_37 · Cited by 213 · see citing works
Zero-Shot Semantic SegmentationPreprint
Bucher, M., Vu, T.-H., Cord, M., & Pérez, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.00817 · Cited by 212 · see citing works
SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances
Pan, Y., Gao, B., Mei, J., Geng, S., Li, C., & Zhao, H. (2020) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv47402.2020.9304596 · Cited by 210 · see citing works
A Review on Deep Learning Techniques for 3D Sensed Data Classification
Griffiths, D., & Boehm, J. (2019) · Remote Sensing · doi:10.3390/rs11121499 · Cited by 197 · see citing works
TerraMobilita/iQmulus urban point cloud analysis benchmark
Vallet, B., Brédif, M., Serna, A., Marcotegui, B., & Nicolas Paparoditis, A. (2015) · Computers & Graphics · doi:10.1016/j.cag.2015.03.004 · Cited by 196 · see citing works
SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving
Aksoy, E. E., Baci, S., & Cavdar, S. (2020) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv47402.2020.9304694 · Cited by 194 · see citing works
Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks
Jampani, V., Kiefel, M., & Gehler, P. (2016) · 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.2016.482 · Cited by 191 · see citing works
Self-Supervised Deep Learning on Point Clouds by Reconstructing SpacePreprint
Sauder, J. F., & Sievers, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.08396 · Cited by 182 · see citing works
Yue, Y., Smith, B., Batty, C., Zheng, C., & Grinspun, E. (2015) · ACM Transactions on Graphics · doi:10.1145/2751541 · Cited by 179 · see citing works
A Review on Deep Learning Techniques for 3D Sensed Data Classification
Griffiths, D., & Boehm, J. (2019) · UCL Discovery (University College London) · Cited by 176 · see citing works
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network
Hong, S., Oh, J., Lee, H., & Han, B. (2016) · 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.2016.349 · Cited by 174 · see citing works
A*3D Dataset: Towards Autonomous Driving in Challenging Environments
Pham, Q.-H., Sevestre, P., Pahwa, R. S., Zhan, H., Pang, C. H., Chen, Y., Mustafa, A., Chandrasekhar, V., & Lin, J. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra40945.2020.9197385 · Cited by 170 · see citing works
Effective Use of Synthetic Data for Urban Scene Semantic Segmentation
Saleh, F. S., Aliakbarian, M. S., Salzmann, M., Petersson, L., & Álvarez, J. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_6 · Cited by 169 · see citing works
Monocular Semantic Occupancy Grid Mapping With Convolutional Variational Encoder–Decoder Networks
Lu, C., van de Molengraft, R., & Dubbelman, G. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2891028 · Cited by 169 · see citing works
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer
Xie, J., Kiefel, M., Sun, M., & Geiger, A. (2016) · 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.2016.401 · Cited by 166 · see citing works
Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic SegmentationPreprint
Zhou, H., Zhu, X., Song, X., Ma, Y., Wang, Z., Li, H., & Lin, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.01550 · Cited by 165 · see citing works
Augmented LiDAR Simulator for Autonomous Driving
Fang, J., Zhou, D., Yan, F., Zhao, T., Zhang, F., Ma, Y., Wang, L., & Yang, R. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2969927 · Cited by 163 · see citing works
Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation
Saleh, F. Q., Aliakbarian, M. S., Salzmann, M., Petersson, L., Gould, S. J., & Álvarez, J. M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46484-8_25 · Cited by 162 · see citing works
Alonso, Í., Riazuelo, L., Montesano, L., & Murillo, A. C. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3007440 · Cited by 160 · see citing works
Few-shot 3D Point Cloud Semantic Segmentation
Zhao, N., Chua, T., & Lee, G. H. (2021) · 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/cvpr46437.2021.00876 · Cited by 142 · see citing works
Distinct Class-Specific Saliency Maps for Weakly Supervised Semantic Segmentation
Shimoda, W., & Yanai, . (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_14 · Cited by 140 · see citing works
3D terrestrial LIDAR classifications with super-voxels and multi-scale Conditional Random Fields
Lim, E. H., & Suter, D. (2009) · Computer-Aided Design · doi:10.1016/j.cad.2009.02.010 · Cited by 132 · see citing works
STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling
He, Y., Chiu, W.-C., Keuper, M., & Fritz, M. (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.2017.757 · Cited by 127 · see citing works
PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point CloudPreprint
Wang, Y., Shi, T., Peng, Y., Tai, L., & Liu, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.06288 · Cited by 117 · see citing works
Liu, F., Li, S., Zhang, L., Zhou, C., Ye, R., Wang, Y., & Lu, J. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.605 · Cited by 117 · see citing works
Find your own way: Weakly-supervised segmentation of path proposals for urban autonomy
Barnes, D., Maddern, W., & Posner, I. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989025 · Cited by 117 · see citing works
Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds
Engelmann, F., Kontogianni, T., & Leibe, B. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197503 · Cited by 109 · see citing works
Deep tracking in the wild: End-to-end tracking using recurrent neural networks
Dequaire, J., Ondrúška, P., Rao, D., Wang, D. Z., & Posner, I. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917710543 · Cited by 102 · see citing works
Automatic Generation of Synthetic LiDAR Point Clouds for 3-D Data Analysis
Wang, F., Zhuang, Y., Gu, H., & Hu, H. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2906416 · Cited by 102 · see citing works
Fast semantic segmentation of 3D point clouds using a dense CRF with learned parameters
Wolf, D., Prankl, J., & Vincze, M. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139875 · Cited by 98 · see citing works
Cortinhal, T., Tzelepis, G., & Aksoy, E. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.03653 · Cited by 96 · see citing works
Know What Your Neighbors Do : 3D Semantic Segmentation of Point Clouds
Engelmann, F., Kontogianni, T., Schult, J., & Leibe, B. (2019) · RWTH Publications (RWTH Aachen) · Cited by 96 · see citing works
LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices
Roşu, R. A., Schütt, P., Quenzel, J., & Behnke, S. (2020) · Robotics: Science and Systems · doi:10.15607/rss.2020.xvi.006 · Cited by 94 · see citing works
Differentiable Meta-Learning Model for Few-Shot Semantic Segmentation
Tian, P., Wu, Z., Qi, L., Wang, L., Shi, Y., & Gao, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6887 · Cited by 93 · see citing works
AN APPROACH TO EXTRACT MOVING OBJECTS FROM MLS DATA USING A VOLUMETRIC BACKGROUND REPRESENTATION
Gehrung, J., Hebel, M., Arens, M., & Stilla, U. (2017) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-1-w1-107-2017 · Cited by 89 · see citing works
Robust 3D scan point classification using associative Markov networks
Triebel, R., Kersting, K., & Burgard, W. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2006.1642094 · Cited by 84 · see citing works
Free Supervision from Video Games
Krähenbühl, P. (2018) · 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.2018.00312 · Cited by 82 · see citing works
CEREALS - Cost-Effective REgion-based Active Learning for Semantic SegmentationPreprint
Mackowiak, R., Lenz, P., Ghori, O., Diego, F., Lange, O., & Rother, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.09726 · Cited by 80 · see citing works
Efficient Convolutions for Real-Time Semantic Segmentation of 3D Point Clouds
Zhang, W., Luo, W., & Urtasun, R. (2018) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2018.00053 · Cited by 79 · see citing works
Boosting LiDAR-Based Semantic Labeling by Cross-modal Training Data Generation
Piewak, F., Pinggera, P., Schäfer, M., Peter, D., Schwarz, B., Schneider, N., Enzweiler, M., Pfeiffer, D., & Zöllner, M. J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11024-6_39 · Cited by 78 · see citing works
Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using Semi-Supervised Learning
Mei, J., Gao, B., Xu, D., Yao, W., Zhao, X., & Zhao, H. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2919741 · Cited by 78 · see citing works
Domain Transfer for Semantic Segmentation of LiDAR Data using Deep Neural Networks
Langer, F., Milioto, A., Haag, A., Behley, J., & Stachniss, C. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341508 · Cited by 76 · see citing works
A practical approach to robotic design for the DARPA Urban Challenge
Patz, B. J., Papelis, Y. E., Pillat, R., Stein, G., & Harper, D. (2008) · Journal of Field Robotics · doi:10.1002/rob.20251 · Cited by 70 · see citing works
Semantic Labeling of Mobile LiDAR Point Clouds via Active Learning and Higher Order MRF
Luo, H., Wang, C., Wen, C., Chen, Z., Zai, D., Yu, Y., & Li, J. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2802935 · Cited by 70 · see citing works
LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation
Jiang, P., & Saripalli, S. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561255 · Cited by 67 · see citing works
Improving 3D Object Detection Through Progressive Population Based Augmentation
Cheng, S., Leng, Z., Cubuk, E. D., Zoph, B., Bai, C., Ngiam, J., Song, Y., Caine, B., Vasudevan, V., Li, C., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58589-1_17 · Cited by 62 · see citing works
Zimmer, W. M. X., Rangesh, A., & Trivedi, M. M. (2019) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2019.8814071 · Cited by 58 · see citing works
SynthCity: A large scale synthetic point cloudPreprint
Griffiths, D., & Boehm, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.04758 · Cited by 55 · see citing works
Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds
Engelmann, F., Kontogianni, T., Schult, J., & Leibe, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11015-4_29 · Cited by 50 · see citing works
Cross-Sensor Deep Domain Adaptation for LiDAR Detection and Segmentation
Rist, C. B., Enzweiler, M., & Gavrila, D. M. (2019) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2019.8814047 · Cited by 49 · see citing works
Super-sensor for 360-degree environment perception: Point cloud segmentation using image features
Varga, R., Costea, A. D., Florea, H., Giosan, I., & Nedevschi, S. (2017) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2017.8317846 · Cited by 46 · see citing works
Classification of Outdoor 3D Lidar Data Based on Unsupervised Gaussian Mixture Models
Maligo, A., & Lacroix, S. (2016) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2614923 · Cited by 44 · see citing works
3D computer vision based on machine learning with deep neural networks: A review
Vodrahalli, K., & Bhowmik, A. K. (2017) · Journal of the Society for Information Display · doi:10.1002/jsid.617 · Cited by 42 · see citing works
Biasutti, P., Lepetit, V., Aujol, J., Brédif, M., & Bugeau, A. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00123 · Cited by 41 · see citing works
LSANet: Feature Learning on Point Sets by Local Spatial Aware LayerPreprint
Chen, L.-Z., Li, X., Fan, D.-P., Wang, K., Lu, S.-P., & Cheng, M.-M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.05442 · Cited by 39 · see citing works
Mark Yourself: Road Marking Segmentation via Weakly-Supervised Annotations from Multimodal Data
Bruls, T., Maddern, W., Morye, A. A., & Newman, P. M. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460952 · Cited by 38 · see citing works
Semantic Grid Estimation with a Hybrid Bayesian and Deep Neural Network Approach
Erkent, Ö., Wolf, C., Laugier, C., González, D., & Romero-Cano, V. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593434 · Cited by 34 · see citing works
Li, Z., Zhang, L., Zhong, R., Fang, T., Zhang, L., & Zhang, Z. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2628399 · Cited by 33 · see citing works
PointAtrousNet: Point Atrous Convolution for Point Cloud Analysis
Pan, L., Wang, P., & Chew, C. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2927948 · Cited by 33 · see citing works
Zhiheng, K., & Li, N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.03299 · Cited by 29 · see citing works
Incorporating Human Domain Knowledge in 3-D LiDAR-Based Semantic Segmentation
Mei, J., & Zhao, H. (2019) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2019.2955851 · Cited by 27 · see citing works
Improving Weakly-Supervised Object Localization By Micro-Annotation
Kolesnikov, A., & Lampert, C. H. (2016) · British Machine Vision Conference (BMVC) · doi:10.5244/c.30.92 · Cited by 24 · see citing works
Season-Invariant Semantic Segmentation with a Deep Multimodal Network
Kim, D.-K., Maturana, D., Uenoyama, M., & Scherer, S. (2017) · Springer proceedings in advanced robotics · doi:10.1007/978-3-319-67361-5_17 · Cited by 24 · see citing works
DeepTemporalSeg: Temporally Consistent Semantic Segmentation of 3D LiDAR Scans
Dewan, A., & Burgard, W. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197193 · Cited by 23 · see citing works
Weakly supervised semantic segmentation with superpixel embedding
Xing, F., Cambria, E., Huang, W.-B., & Xu, Y. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532562 · Cited by 22 · see citing works
Pan, L., Chew, C., & Lee, G. H. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197499 · Cited by 22 · see citing works
RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloudPreprint
Biasutti, P., Bugeau, A., Aujol, J., & Brédif, M. (2019) · HAL · doi:10.48550/arxiv.1905.08748 · Cited by 18 · see citing works
Learning a Spatio-Temporal Embedding for Video Instance SegmentationPreprint
Hu, A., Kendall, A., & Cipolla, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.08969 · Cited by 17 · see citing works
xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic SegmentationPreprint
Jaritz, M., Vu, T.-H., de Charette, R., Wirbel, É., & Pérez, P. (2019) · arXiv (Cornell University) · Cited by 16 · see citing works
DAR-Net: Dynamic Aggregation Network for Semantic Scene SegmentationPreprint
Zhao, Z., Liu, M., & Ramani, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.12022 · Cited by 14 · see citing works
Chen, X., Zhang, C., Lin, G., & Han, J. G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.14255 · Cited by 14 · see citing works
LiSeg: Lightweight Road-object Semantic Segmentation In 3D LiDAR Scans For Autonomous Driving
Zhang, W., Zhou, C., Yang, J., & Huang, K. (2018) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2018.8500701 · Cited by 13 · see citing works
Semantic Segmentation of Sparsely Annotated 3D Point Clouds by Pseudo-Labelling
Xu, K., Yao, Y., Murasaki, K., Ando, S., & Sagata, A. (2019) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2019.00058 · Cited by 13 · see citing works
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 · see citing works
Joint Margin, Cograph, and Label Constraints for Semisupervised Scene Parsing From Point Clouds
Mei, J., Zhang, L., Wang, Y., Zhu, Z., & Ding, H. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2811748 · Cited by 10 · see citing works
Flagged first, then by citations
14 of the 217 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Muhammad, K., Hussain, T., Ullah, H., Ser, J. D., Rezaei, M., Kumar, N., Hijji, M., Bellavista, P., & de Albuquerque, V. H. C. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3207665 · Cited by 221 · see citing works
Delivering Arbitrary-Modal Semantic Segmentation
Zhang, J., Liu, R., Shi, H., Yang, K., Reiß, S., Peng, K., Fu, H., Wang, K., & Stiefelhagen, R. (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/cvpr52729.2023.00116 · Cited by 202 · see citing works
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP
Chen, R., Liu, Y., Kong, L., Zhu, X., Ma, Y., Li, Y., Hou, Y., Qiao, Y., & Wang, W. (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/cvpr52729.2023.00678 · Cited by 161 · see citing works
Review on Panoramic Imaging and Its Applications in Scene Understanding
Gao, S., Yang, K., Shi, H., Wang, K., & Bai, J. Y. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3216675 · Cited by 121 · see citing works
LaserMix for Semi-Supervised LiDAR Semantic Segmentation
Kong, L., Ren, J., Pan, L., & Liu, Z. (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/cvpr52729.2023.02079 · Cited by 114 · see citing works
3D Semantic Scene Completion: A Survey
Roldão, L., de Charette, R., & Verroust-Blondet, A. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-021-01504-5 · Cited by 113 · see citing works
A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook
Liu, M., Yurtsever, E., Fossaert, J., Zhou, X., Zimmer, W. M. X., Cui, Y., Žagar, B. L., & Knoll, A. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3394735 · Cited by 114 · see citing works
A new weakly supervised approach for ALS point cloud semantic segmentation
Wang, P., & Yao, W. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.04.016 · Cited by 84 · see citing works
UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase
Liu, Y., Chen, R., Li, X. S., Kong, L., Yang, Y., Xia, Z., Bai, Y., Zhu, X., Ma, Y., Li, Y., et al. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01980 · Cited by 80 · see citing works
Recent Advancements in Learning Algorithms for Point Clouds: An Updated Overview
Camuffo, E., Mari, D., & Milani, S. (2022) · Sensors · doi:10.3390/s22041357 · Cited by 73 · see citing works
MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding
Peng, K., Fei, J., Yang, K., Roitberg, A., Zhang, J., Bieder, F., Heidenreich, P., Stiller, C., & Stiefelhagen, R. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3145588 · Cited by 68 · see citing works
Deep transfer learning for intelligent vehicle perception: A survey
Liu, X., Li, J., Ma, J., Sun, H., Xu, Z., Zhang, T., & Yu, H. (2023) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2023.100125 · Cited by 69 · see citing works
Rizzoli, G., Barbato, F., & Zanuttigh, P. (2022) · Technologies · doi:10.3390/technologies10040090 · Cited by 58 · see citing works
A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors
Gomes, T., Matias, D., Campos, A., Cunha, L., & Roriz, R. (2023) · Sensors · doi:10.3390/s23020601 · Cited by 54 · see citing works
Noichl, F., Collins, F. C., Braun, A., & Borrmann, A. (2024) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13153 · Cited by 47 · see citing works
Zhou, S., Xu, H., Zhang, G., Ma, T., & Yang, Y. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3183889 · Cited by 45 · see citing works
TransRVNet: LiDAR Semantic Segmentation With Transformer
Cheng, H.-X., Han, X.-F., & Xiao, G. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3248117 · Cited by 46 · see citing works
Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving
Kong, L., Xu, X., Ren, J., Zhang, W., Pan, L., Chen, K., Ooi, W. T., & Liu, Z. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3535625 · Cited by 43 · see citing works
Deep learning-based semantic segmentation of urban-scale 3D meshes in remote sensing: A survey
Adam, J. M., Liu, W., Zang, Y., Afzal, M. K., Bello, S. A., Muhammad, A. U., Wang, C., & Li, J. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103365 · Cited by 35 · see citing works
Deep Learning on 3D Semantic Segmentation: A Detailed Review
Betsas, T., Georgopoulos, A., Doulamis, A. D., & Grussenmeyer, P. (2025) · Remote Sensing · doi:10.3390/rs17020298 · Cited by 37 · see citing works
Yatbaz, H. Y., Dianati, M., & Woodman, R. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3315070 · Cited by 28 · see citing works
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 · see citing works
Category-Level Adversaries for Outdoor LiDAR Point Clouds Cross-Domain Semantic Segmentation
Yuan, Z., Wen, C., Cheng, M., Su, Y., Liu, W., Yu, S., & Wang, C. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3219853 · Cited by 24 · see citing works
Survey on Fish-Eye Cameras and Their Applications in Intelligent Vehicles
Qian, Y., Yang, M. Z., & Dolan, J. M. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3210409 · Cited by 23 · see citing works
SLiDE: Self-supervised LiDAR De-snowing Through Reconstruction Difficulty
Bae, G., Kim, B.-J., Ahn, S., Min, J., & Shim, I. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19842-7_17 · Cited by 22 · see citing works
Wang, J., Deng, H., Wang, Y., Xie, J., Zhang, H., Li, Y., & Guo, S. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102320 · Cited by 22 · see citing works
Semantic segmentation of bridge components and road infrastructure from mobile LiDAR data
Lin, Y.-C., & Habib, A. F. (2022) · ISPRS Open Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.ophoto.2022.100023 · Cited by 21 · see citing works
Cao, R., Zhang, D., Wei, P.-J., Ding, Y., Zheng, C.-H., Tan, D., & Zhou, C. Q. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3380054 · Cited by 18 · see citing works
Understanding the Challenges When 3D Semantic Segmentation Faces Class Imbalanced and OOD Data
Pan, Y., Xie, F., & Zhao, H. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3256442 · Cited by 17 · see citing works
DO-SA&R: Distant Object Augmented Set Abstraction and Regression for Point-Based 3D Object Detection
He, X., Wang, Z., Lin, J., Nai, K., Yuan, J., & Li, Z. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3326394 · Cited by 16 · see citing works
Deep Hierarchical Learning for 3D Semantic Segmentation
Li, C., Liu, Y., Li, X., Zhang, Y., Li, T., & Yuan, J. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02387-6 · Cited by 16 · see citing works
An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation
Gao, B., Zhao, X., & Zhao, H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3218403 · Cited by 16 · see citing works
Korus, K., Czerniawski, T., & Salamak, M. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105126 · Cited by 15 · see citing works
SFPNet: Sparse Focal Point Network for Semantic Segmentation on General LiDAR Point Clouds
Wang, Y., Zhao, W., Cao, C., Deng, T., Wang, J., & Chen, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72652-1_24 · Cited by 16 · see citing works
LiDAR in Connected and Autonomous Vehicles: Perception, Threat Model, and Defense
Khan, M. A., Menouar, H., Abdallah, M. M., & Abu‐Dayya, A. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3510787 · Cited by 14 · see citing works
COLA: COarse-LAbel Multisource LiDAR Semantic Segmentation for Autonomous Driving
Sanchez, J., Deschaud, J., & Goulette, F. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3543302 · Cited by 14 · see citing works
Morphology-Guided Network via Knowledge Distillation for RGB-D Mirror Segmentation
Zhou, W., Cai, Y., & Qiang, F. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3404654 · Cited by 13 · see citing works
Kim, Y., Kim, J., Kim, J., & Kim, H. (2025) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-6383 · Cited by 11 · see citing works
He, X., Li, X., Xu, Q., Hu, Y., & Sun, Z. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126572 · Cited by 11 · see citing works
Towards a Data‐Driven Digital Twin AI‐Based Architecture for Self‐Driving Vehicles
Babaei, P., Riahinia, N., Omid Mahdi Ebadati, E., & Azimi, A. (2025) · IET Intelligent Transport Systems · doi:10.1049/itr2.70017 · Cited by 10 · see citing works
Hachisuka, S., Tono, A., & Fisher, M. (2023) · Computing in construction · doi:10.35490/ec3.2023.284 · Cited by 11 · see citing works
Vehicle-mounted imaging lidar with nonuniform distribution of instantaneous field of view
Wang, X., & Zhu, J. (2023) · Optics & Laser Technology · doi:10.1016/j.optlastec.2023.110063 · Cited by 11 · see citing works
Huch, S., & Lienkamp, M. (2023) · Sensors · doi:10.3390/s23249913 · Cited by 10 · see citing works
4D Contrastive Superflows are Dense 3D Representation Learners
Xu, X., Kong, L., Shuai, H., Zhang, W., Pan, L., Chen, K., Liu, Z., & Liu, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73232-4_4 · Cited by 10 · see citing works
LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes
Xu, X., Kong, L., Shuai, H., Pan, L., Liu, Z., & Liu, Q. (2025) · 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/cvpr52734.2025.02549 · Cited by 12 · see citing works
Explaining 3D Semantic Segmentation Through Generative AI ‐Based Counterfactuals
Rozajac, D., Lukač, N., Schweng, S., Gollob, C., Nothdurft, A., Stampfer, K., Ser, J. D., & Holzinger, A. (2025) · Expert Systems · doi:10.1111/exsy.70163 · Cited by 10 · see citing works
LiDAR-Based SLAM under Semantic Constraints in Dynamic Environments
Wang, W., You, X., Zhang, X., Chen, L., Zhang, L., & Liu, X. (2021) · Remote Sensing · doi:10.3390/rs13183651 · Cited by 10 · see citing works
Camuffo, E., & Milani, S. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00243 · Cited by 10 · see citing works
Multisensor Fusion Network for Unstructured Scene Segmentation With Surface Normal Incorporated
Feng, Y., Li, X., Ni, P., Liu, X., & Jiang, T. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3370255 · Cited by 9 · see citing works
Barnefske, E., & Sternberg, H. (2022) · IEEE Access · doi:10.1109/access.2022.3233411 · Cited by 10 · see citing works
Recent Developments on Drivable Area Estimation: A Survey and a Functional Analysis
Hortelano, J. L., Villagrá, J., Godoy, J., & Jiménez, V. (2023) · Sensors · doi:10.3390/s23177633 · Cited by 8 · see citing works
LiDAR-Based Real-Time Panoptic Segmentation via Spatiotemporal Sequential Data Fusion
Wang, W., You, X., Yang, J., Su, M., Zhang, L., Yang, Z., & Kuang, Y. (2022) · Remote Sensing · doi:10.3390/rs14081775 · Cited by 7 · see citing works
Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving
Mei, J., Zhou, J., & Hu, Y. H. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160674 · Cited by 6 · see citing works
Understanding Novice's Annotation Process For 3D Semantic Segmentation Task With Human-In-The-Loop
Kim, Y., Lee, E., Lee, Y.-J., & Oh, U. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640543.3645150 · Cited by 5 · see citing works
PF3Det: A Prompted Foundation Feature Assisted Visual Lidar 3D Detector
Li, K., Zhang, T., Peng, K., & Wang, G. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00363 · Cited by 5 · see citing works
Representation Learning for Wafer Pattern Recognition in Semiconductor Manufacturing Process
Song, S., & Back, J.-G. (2023) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic57133.2023.10067020 · Cited by 5 · see citing works
LiDAR-camera Cooperative Semantic Segmentation
Guan, H., Song, C., & Zhang, Z. (2025) · Machine Intelligence Research · doi:10.1007/s11633-024-1508-2 · Cited by 4 · see citing works
ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation
Zhang, Z., Yang, X., Zhang, W., & Jin, C. (2024) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme57554.2024.10687769 · Cited by 4 · see citing works
Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understanding
Kong, L., Xu, X., Cen, J., Zhang, W., Pan, L., Chen, K., & Liu, Z. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00198 · Cited by 4 · see citing works
Shen, L., Cao, Y., Wei, X., & Li, S. C. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130241 · Cited by 3 · see citing works
ANZIL: Attention-based network for zero-risk inspection of LiDAR point cloud in self-driving cars
Jati, G., Molan, M., Barchi, F., Bartolini, A., Mercurio, G., & Acquaviva, A. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128412 · Cited by 3 · see citing works
Generation of synthetic Point Clouds for MEMS LiDAR SensorPreprint
Berens, F., Reischl, M., & Elser, S. (2022) · doi:10.36227/techrxiv.19615563.v2 · Cited by 3 · see citing works
Handling intra-class imbalance in part-segmentation of different wheat cultivars
Reena, R., Doonan, J. H., Williams, K. S., Corke, F. M. K., Zhang, H., & Liu, Y. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109826 · Cited by 2 · see citing works
Prathiba, S. B., Kumar, S. V., Anandhan, D. K., Kumar, A. S. S., Selvaraj, A. K., & Rodrigues, J. J. P. C. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3555367 · Cited by 2 · see citing works
He, X., Li, X., Li, B., Xu, Z., Xu, Q., & Lu, H. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.104019 · Cited by 2 · see citing works
Carós, M., Just, A., Seguí, S., & Vitria, J. M. (2026) · ISPRS Open Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.ophoto.2026.100119 · Cited by 3 · see citing works
A Review of Ground Segmentation Methods for Vehicle-Mounted LiDAR Point Clouds
Mijit, T., Ahat, A., & Hamdulla, A. (2023) · IEEE International Conference on Pattern Recognition and Machine Learning (PRML) · doi:10.1109/prml59573.2023.10348257 · Cited by 2 · see citing works
A transformer-based real-time LiDAR semantic segmentation method for restricted mobile devices
Liu, C., Zhao, J., & Sun, N. (2024) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2024.01.033 · Cited by 2 · see citing works
A LIDAR-based Traffic Data Classification Framework for Indian Urban Traffic
Prajwal Shettigar, J., Tangirala, A. K., & Vanajakshi, L. (2024) · International Journal of Intelligent Transportation Systems Research · doi:10.1007/s13177-024-00436-x · Cited by 2 · see citing works
LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving
Kong, L., Xu, X., Liu, Y.-C., Cen, J., Chen, R.-N., Zhang, W.-W., Pan, L., Chen, K., & Liu, Z.-W. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3617126 · Cited by 23 · see citing works
Generation of synthetic Point Clouds for MEMS LiDAR SensorPreprint
Berens, F., Elser, S., & Reischl, M. (2022) · doi:10.36227/techrxiv.19615563 · Cited by 2 · see citing works
He, G., Liu, Y., & Tan, Q. (2023) · International Conference on Information Science and Technology · doi:10.1109/icist59754.2023.10367165 · Cited by 1 · see citing works
Pseudo Multi-Modal Approach to LiDAR Semantic Segmentation
Kim, K. (2024) · Sensors · doi:10.3390/s24237840 · Cited by 1 · see citing works
Physically Accurate Lidar Simulation for Automotive Digital Twins
Dimitrievski, M., Hamme, D. V., & Philips, W. R. (2024) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/sensors60989.2024.10784869 · Cited by 1 · see citing works
TA-TOS: Terrain-Aware Tiny Obstacle Segmentation Based on MRF Road Modeling Using 3-D LiDAR Scans
Ming, N., Qian, Y., Feng, C., Wang, C., & Yang, M. Z. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3574769 · Cited by 1 · see citing works
Mortlock, T., Chen, L., Smereka, J. M., Khargonekar, P. P., & Faruque, M. A. A. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3612873 · Cited by 1 · see citing works
Sevinç, H. K., & Karas, I. R. (2026) · Geomatics · doi:10.3390/geomatics6030043 · Cited by 1 · see citing works
Using mobile laser scanner and imagery for urban management applications
González‐Barbosa, J., Flores-Rodrıguez, K. L., Ornelas-Rodríguez, F.-J., Trujillo-Romero, F., González-Barbosa, E.-A., & Hurtado‐Ramos, J. B. (2022) · IAES International Journal of Robotics and Automation (IJRA) · doi:10.11591/ijra.v11i2.pp89-110 · Cited by 1 · see citing works
3D Reconstruction of 2D Semantic Segmentation Using Sensor Fusion Based Trans-Unet
Lee, Y., Jeon, J., Yoon, J., & Song, K. (2023) · Transactions of Korean Society of Automotive Engineers · doi:10.7467/ksae.2023.31.4.255 · Cited by 1 · see citing works
Semantic and Moving Object Segmentation-assisted LiDAR Odometry and Mapping
Wang, F., Sun, C., Zhong, G., & Liang, W. (2023) · International Conference on Electrical Engineering, Control and Robotics (EECR) · doi:10.1109/eecr56827.2023.10150083 · Cited by 1 · see citing works
SphNet: A Spherical Network for Semantic Pointcloud Segmentation
Bernreiter, L., Ott, L., Siegwart, R., & Cadena, C. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160412 · Cited by 1 · see citing works
Performance Analysis of LiDAR Data Processing on Multi-Core CPU and GPU Architectures
Alzyout, M. S., Nounou, A. A. A., Tikkisetty, Y. N., & Alawneh, S. G. (2024) · IEEE International Conference on Computing and Machine Intelligence (ICMI) · doi:10.1109/icmi60790.2024.10586050 · Cited by 1 · see citing works
From Purpose to Technology: A Requirements-Driven Approach to Designing Digital Twin Implementations
Noichl, F., Collins, F. C., Pan, Y., Mafipour, M. S., Mehranfar, M., & Borrmann, A. (2024) · American Society of Civil Engineers eBooks · doi:10.1061/9780784485606.ch5 · Cited by 1 · see citing works
DentAssignNet: Assignment Network for Dental Cast Labeling in the Presence of Dental Abnormalities
Dascalu, T., Ramezanzade, S., Bakhshandeh, A., Bjørndal, L., Iurcov, R. O. C., Vrtovec, T., & Ibragimov, B. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3549685 · Cited by 1 · see citing works
3DGENie: Synthetic point clouds for semantic segmentation in realistic virtual environments
Yaghi, A., Tekli, J., Kamradt, M., & Couturier, R. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20973-1 · Cited by 1 · see citing works
Generation of synthetic Point Clouds for MEMS LiDAR SensorPreprint
Berens, F., Reischl, M., & Elser, S. (2022) · doi:10.36227/techrxiv.19615563.v1
Field Test Bed for Evaluating Embedded Vehicle Sensors with Indiana Companies
Habib, A. F., Bullock, D. M., Lin, Y.-C., Manish, R., & Ravi, R. (2023) · doi:10.5703/1288284317385
Parallel Multi-Task Network from 2d Image to 3d Semantic Scene CompletionPreprint
Lu, Z., Cao, B., & Hu, Q. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4634961
MVSRF: Point cloud semantic segmentation and optimization method for granular construction objects
Zhang, L., Liu, G., Lu, J., & Wang, C. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06326-3
LiDAR Data Augmentation for Semantic Segmentation: A Statistical Intensity Model
Aguilar-Pérez, P., & Redondo, R. P. D. (2025) · Journal of Advances in Information Technology · doi:10.12720/jait.16.2.223-232
Methodology for Scalable LiDAR Datasets
Gutierrez-Cabello, G. S., Jiménez, F., & Talavera, E. (2025) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv64158.2025.11097680
Cop, K. P., Sułek, B., & Trzcinski, T. P. (2025) · Foundations of Computing and Decision Sciences · doi:10.2478/fcds-2025-0013
A Semantic-Associated Factor Graph Model for LiDAR-Assisted Indoor Multipath Localization
Liu, B., Han, K., Deng, Z., & Guo, G. (2026) · Sensors · doi:10.3390/s26010346
Liu, B., Han, K., Deng, Z., & Ding, Z. (2026) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2026.3663277
Γκίλλας, Α., Piperigkos, N., Lalos, A. S., & Nguyen, D.-V. (2026) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2026.3664354
Selvarajan, N. P., Megalingam, R. K., & P, A. T. (2025) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon67554.2025.11378941
Park, S., Ju, S., Hong, J., & Heo, J. (2026) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-7153
Wang, Y., Wu, L., Zhang, Z., Huo, L., Feng, J., Wang, J., & Jin, J. (2026) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2026.3673943 · Cited by 1 · see citing works
Wang, T., Wang, N. C., Li, S., Jin, Z., Xiao, J., Miao, Y., Sun, Y., (10885), H. L., & Zhang, M. (2026) · Engineering · doi:10.1016/j.eng.2026.01.030
Cluster-based Pseudo-labeling for Semi-Supervised LiDAR Semantic Segmentation
Guo, Q., Li, S., Geng, J., Xie, B., Shan, J., & Li, W. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00068
- Authors
- Gao, B., Pan, Y., Li, C., Geng, S., & Zhao, H.
- Affiliations
- Peking University (CN)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2022-07
- Volume
- 23
- Issue
- 7
- Pages
- 6063-6081
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W3160425077
- Cited by
- 105
- References
- 217
- Citations by year
- 2026: 13 · 2025: 26 · 2024: 19 · 2023: 27 · 2022: 19 · 2021: 1
- FWCI
- 5.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- Funders
- Development Program of China (2017YFB1002601)
- National Natural Science Foundation of China (61973004)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- 3D semantic segmentation, 3D LiDAR, dataset quality, data bias, domain gap, semantic gap, autonomous driving, robotics, deep learning
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 ›
Are you an author, or do you see a problem with this page? Request a review
