PCT: Point cloud transformer
Open accessAbstract
The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer (PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation, semantic segmentation, and normal estimation tasks.
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Cross-lingual gap should be considered for the transferability of Transformer based multimodal learning.
“Cross-lingual gap also should be considered for the transferability of Transformer based multimodal learning, e.g., universal cross-lingual generalization from English to non-English multimodal contexts [206], [277].”
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667 · see citing works
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 21,793 · see citing works
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285 · see citing works
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · 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.1993.341010 · Cited by 14,474 · see citing works
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz682 · Cited by 7,218 · see citing works
Dynamic Graph CNN for Learning on Point Clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M. (2019) · ACM Transactions on Graphics · doi:10.1145/3326362 · Cited by 7,199 · see citing works
Self-citationResidual Attention Network for Image Classification
Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., & Tang, X. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.683 · Cited by 4,073 · see citing works
Self-Attention Generative Adversarial Networks
Zhang, H., Goodfellow, I., Metaxas, D., & Odena, A. (2018) · arXiv · doi:10.48550/arxiv.1805.08318 · Cited by 3,353 · 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
Proceedings of the 2nd International Conference on Neural Information Processing Systems
Touretzky, D. S. (1989) · Cited by 1,413 · see citing works
A scalable active framework for region annotation in 3D shape collections
Yi, L., Kim, V. G., Ceylan, D., Shen, I., Yan, M., Su, H., Lu, C., Qi-xing Huang, A., Sheffer, A., & Guibas, L. J. (2016) · ACM Transactions on Graphics · doi:10.1145/2980179.2980238 · Cited by 1,209 · see citing works
Proceedings of the 24th annual meeting on Association for Computational Linguistics -
Biermann, A. W. (1986) · doi:10.3115/981131 · Cited by 1,086 · see citing works
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
Liu, Y., Fan, B., Xiang, S., & Pan, 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.2019.00910 · Cited by 1,042 · 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
Point convolutional neural networks by extension operators
Atzmon, M., Maron, H., & Lipman, Y. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201301 · Cited by 559 · see citing works
Visual Transformers: Token-based Image Representation and Processing for Computer VisionPreprint
Wu, B., Xu, C., Dai, X., Wan, A., Zhang, P., Yan, Z., Tomizuka, M., Gonzalez, J., Keutzer, K., & Vajda, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.03677 · Cited by 529 · see citing works
Attentional ShapeContextNet for Point Cloud Recognition
Xie, S., Liu, S., Chen, Z., & Tu, Z. (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.2018.00484 · Cited by 362 · see citing works
Liu, X., Han, Z., Liu, Y.-S., & Zwicker, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018778 · Cited by 328 · see citing works
Monte Carlo convolution for learning on non-uniformly sampled point clouds
Hermosilla, P., Ritschel, T., Vázquez, P.-P., Vinacua, À., & Ropinski, T. (2018) · ACM Transactions on Graphics · doi:10.1145/3272127.3275110 · Cited by 203 · see citing works
Flagged first, then by citations
Research works printed but not linked: 21
- Charles, R. Q.; Hao, S.; Mo, K. C.; Guibas, L. J. PointNet: Deep learning on point sets for 3D classification and segmentation. IN: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 77-85, 2017.
- Tchapmi, L. P.; Choy, C. B.; Armeni, I.; Gwak, J.; Savarese, S. SEGCloud: Semantic segmentation of 3D point clouds. In: Proceedings of the International Conference on 3D Vision, 537-547, 2017.
- Wu, W. X.; Qi, Z.; Fuxin, L. PointConv: Deep convolutional networks on 3D point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9613-9622, 2019.
- Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; Polosukhin, I. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing, 6000-6010, 2017.
- Bruna, J.; Zaremba, W.; Szlam, A.; LeCun, Y. Spectral networks and locally connected networks on graphs. In: Proceedings of the International Conference on Learning Representations, 2014.
- Hu, S.-M.; Liang, D.; Yang, G.-Y.; Yang, G.-W.; Zhou, W.-Y. Jittor: A novel deep learning framework with meta-operators and unified graph execution. Science China Information Sciences Vol. 63, No. 12, Article No. 222103, 2020.
- Bahdanau, D.; Cho, K. H.; Bengio, Y. Neural machine translation by jointly learning to align and translate. In: Proceedings of the 3rd International Conference on Learning Representations, 2015.
- Lin, Z.; Feng, M.; dos Santos, C. N.; Yu, M.; Xiang, B.; Zhou, B.; Bengio, Y. A structured self- attentive sentence embedding. In: Proceedings of the International Conference on Learning Representations, 2017.
- Yang, Z.; Dai, Z.; Yang, Y.; Carbonell, J. G.; Salakhutdinov, R.; Le, Q. V. XLNet: Generalized autoregressive pretraining for language understanding. In: Proceedings of the 33rd Conference on Neural Information Processing Systems, 5754-5764, 2019.
- Vedaldi, A.; Bischof, H.; Brox, T.; Frahm, J. M. Eds. Springer Cham, 213-229, 2020.
- Qi, C. R.; Yi, L.; Su, H.; Guibas, L. J. PointNet++: Deep hierarchical feature learning on point sets in a metric space. In: Proceedings of the 31st Conference on Neural Information Processing Systems, 5099-5108, 2017.
- Yang, Y. Q.; Liu, S. L.; Pan, H.; Liu, Y.; Tong, X. PFCNN: Convolutional neural networks on 3D surfaces using parallel frames. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13575-13584, 2020.
- Yan, X.; Zheng, C. D.; Li, Z.; Wang, S.; Cui, S. G. PointASNL: Robust point clouds processing using nonlocal neural networks with adaptive sampling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5588-5597, 2020.
- Hertz, A.; Hanocka, R.; Giryes, R.; Cohen-Or, D. PointGMM: A neural GMM network for point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 12051- 12060, 2020.
- Wang, Y.; Solomon, J. Deep closest point: Learning representations for point cloud registration. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 3522-3531, 2019.
- Li, J. X.; Chen, B. M.; Lee, G. H. SO-net: Self- organizing network for point cloud analysis. In: Proceeding of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9397-9406, 2018.
- Klokov, R.; Lempitsky, V. Escape from cells: Deep kd- networks for the recognition of 3D point cloud models. In: Proceeding of the IEEE International Conference on Computer Vision, 863-872, 2017.
- Le, T.; Duan, Y. PointGrid: A deep network for 3D shape understanding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9204-9214, 2018.
- Zhao, H.; Jiang, L.; Fu, C.; Jia, J. PointWeb: Enhancing local neighborhood features for point cloud processing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5560-5568, 2019.
- Komarichev, A.; Zhong, Z. C.; Hua, J. A-CNN: Annularly convolutional neural networks on point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7413- 7422, 2019.
- Thomas, H.; Qi, C. R.; Deschaud, J. E.; Marcotegui, B.; Goulette, F.; Guibas, L. KPConv: Flexible and deformable convolution for point clouds. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 6410-6419, 2019.
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Park, J., Lee, S., Kim, S., Xiong, Y., & Kim, H. 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/cvpr52729.2023.02089 · Cited by 139 · see citing works
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Hu, S., Liu, Z.-N., Guo, M.-H., Cai, J.-X., Huang, J., Mu, T., & Martin, R. R. (2022) · ACM Transactions on Graphics · doi:10.1145/3506694 · Cited by 140 · see citing works
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Zhang, R., Wang, L., Qiao, Y., Gao, P., & Li, H. (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/cvpr52729.2023.02085 · Cited by 132 · see citing works
SAT-GCN: Self-attention graph convolutional network-based 3D object detection for autonomous driving
Wang, L., Song, Z., Zhang, X., Wang, C., Zhang, G., Zhu, L., Li, J., & Liu, H. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110080 · Cited by 130 · see citing works
PTTR: Relational 3D Point Cloud Object Tracking with Transformer
Zhou, C., Luo, Z., Luo, Y., Liu, T., Pan, L., Cai, Z., Zhao, H., & Lu, S. (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/cvpr52688.2022.00834 · Cited by 130 · see citing works
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Shi, X., Huang, Z., Li, D., Zhang, M., Cheung, K. C., See, S. C. W., Qin, H., Dai, J., & Li, H. (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/cvpr52729.2023.00160 · Cited by 127 · see citing works
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Zhang, R., Wang, L., Wang, Y., Gao, P., Li, H., & Shi, J.-B. (2023) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52729.2023.00517 · Cited by 65 · see citing works
FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer
Liu, Z., Yang, X., Tang, H., Shang, Y., & Han, S. (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/cvpr52729.2023.00122 · Cited by 126 · see citing works
Self-citation3DCTN: 3D Convolution-Transformer Network for Point Cloud Classification
Lu, D., Xie, Q., Gao, K., Xu, L., & Li, J. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3198836 · Cited by 129 · see citing works
PVT: Point‐voxel transformer for point cloud learning
Zhang, C., Wan, H., Shen, X., & Wu, Z. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.23073 · Cited by 133 · see citing works
Learning Discriminative Features by Covering Local Geometric Space for Point Cloud Analysis
Wang, C., Ning, X., Sun, L., Zhang, L., Li, W., & Bai, X. P. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3170493 · Cited by 122 · see citing works
Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space
Liu, Y., Wang, B., Lv, Y.-S., Li, L., & Wang, F. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123432 · Cited by 113 · see citing works
PointCutMix: Regularization strategy for point cloud classification
Zhang, J., Chen, L., Ouyang, B., Liu, B., Zhu, J., Chen, Y., Meng, Y., & Wu, D. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.07.049 · Cited by 119 · see citing works
Neural Architecture Search for Transformers: A Survey
Chitty-Venkata, K. T., Emani, M., Vishwanath, V., & Somani, A. K. (2022) · IEEE Access · doi:10.1109/access.2022.3212767 · Cited by 118 · see citing works
Attention-Based Point Cloud Edge Sampling
Wu, C., Zheng, J., Pfrommer, J., & Beyerer, 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/cvpr52729.2023.00516 · Cited by 116 · see citing works
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Han, X.-F., Jin, Y. J., Cheng, H.-X., & Xiao, G. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3198318 · Cited by 115 · see citing works
Query2Label: A Simple Transformer Way to Multi-Label ClassificationPreprint
Liu, S., Zhang, L., Yang, X., Su, H., & Zhu, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.10834 · Cited by 178 · see citing works
PointMixer: MLP-Mixer for Point Cloud Understanding
Choe, J., Park, C., Rameau, F., Park, J., & Kweon, I. S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19812-0_36 · Cited by 111 · see citing works
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Yu, X., Xu, M., Zhang, Y., Liu, H., Ye, C., Wu, Y., Yan, Z., Zhu, C., Xiong, Z., Liang, T., et al. (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/cvpr52729.2023.00883 · Cited by 111 · see citing works
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Deng, X., Zhang, W., Ding, Q. W., & Zhang, 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/cvpr52729.2023.00912 · Cited by 109 · see citing works
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Li, S., Gao, P., Tan, X., & Wei, 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/cvpr52729.2023.00913 · Cited by 106 · see citing works
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Zheng, Z., Ye, R., Hou, Q., Ren, D., Wang, P., Zuo, W., & Cheng, M. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3248583 · Cited by 103 · see citing works
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Peng, X., Wen, X., Liu, Y.-S., Cao, Y., Wan, P., Zheng, W., & Han, Z. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3217161 · Cited by 100 · see citing works
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Liu, R., Li, Y., Tao, L., Liang, D., & Zheng, H.-T. (2022) · Patterns · doi:10.1016/j.patter.2022.100520 · Cited by 104 · see citing works
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Deng, S., Liang, Z., Sun, L., & Jia, K. (2022) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52688.2022.00826 · Cited by 98 · see citing works
Sarker, S., Sarker, P., Stone, G., Gorman, R., Tavakkoli, A., Bebis, G. N., & Sattarvand, J. (2024) · Machine Vision and Applications · doi:10.1007/s00138-024-01543-1 · Cited by 101 · see citing works
Zhu, S., Guendel, R. G., Yarovoy, A. G., & Fioranelli, F. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3189746 · Cited by 95 · see citing works
Tian, Y., Li, X., Zhang, H., Zhao, C., Li, B., Wang, X., Wang, X., & Wang, F. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3307012 · Cited by 98 · see citing works
AGConv: Adaptive Graph Convolution on 3D Point Clouds
Wei, M., Wei, Z., Zhou, H., Hu, F., Si, H., Chen, Z., Zhu, Z., Qiu, J., Yan, X., Guo, Y., et al. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3238516 · Cited by 94 · see citing works
PU-Transformer: Point Cloud Upsampling Transformer
Qiu, S., Anwar, S., & Barnes, N. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26319-4_20 · Cited by 96 · see citing works
Wang, S., Mei, L., Liu, R., Jiang, W., Yin, Z., Deng, X., & He, T. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3398004 · Cited by 98 · see citing works
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Fan, Z., Song, Z., Liu, H., Lu, Z., He, J., & Du, X. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19934 · Cited by 93 · see citing works
PatchFormer: An Efficient Point Transformer with Patch Attention
Zhang, C., Wan, H., Shen, X., & Wu, Z. (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/cvpr52688.2022.01150 · Cited by 96 · see citing works
LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers
Huang, Z., Zhao, Z., Li, B., & Han, J. (2022) · IEEE transactions on circuits and systems for video technology (Print) · doi:10.1109/tcsvt.2023.3247506 · Cited by 100 · see citing works
DropKey for Vision Transformer
Li, B.-N., Hu, Y.-H., Nie, X.-C., Han, C.-Y., Jiang, X.-J., Guo, T.-D., & Liu, L.-Q. (2023) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52729.2023.02174 · Cited by 81 · see citing works
PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds
Shan, J., Zhou, S., Fang, Z., & Cui, Y. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636821 · Cited by 95 · see citing works
Pei, X., Zheng, X., & Wu, J. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3119137 · Cited by 95 · see citing works
PointAttN: You Only Need Attention for Point Cloud Completion
Wang, J., Cui, Y., Guo, D., Li, J., Liu, Q., & Shen, C. (2024) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v38i6.28356 · Cited by 92 · see citing works
Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model
Han, X., Tang, Y., Wang, Z., & Li, X. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681173 · Cited by 91 · see citing works
A Novel Local–Global Graph Convolutional Method for Point Cloud Semantic Segmentation
Du, Z., Ye, H., & Cao, F. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3155282 · Cited by 87 · see citing works
OcTr: Octree-Based Transformer for 3D Object Detection
Zhou, C., Zhang, Y., Chen, J., & Huang, D. (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/cvpr52729.2023.00500 · Cited by 88 · see citing works
CASSPR: Cross Attention Single Scan Place Recognition
Xia, Y., Gladkova, M., Wang, R., Li, Q., Stilla, U., Henriques, J. F., & Cremers, D. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00777 · Cited by 87 · see citing works
Liang, Z., Zhao, K., Liang, G., Li, S., Wu, Y., & Zhou, Y. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110987 · Cited by 86 · see citing works
DA-RoadNet: A Dual-Attention Network for Road Extraction From High Resolution Satellite Imagery
Wan, J., Xie, Z., Xu, Y., Chen, S., & Qiu, Q. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3083055 · Cited by 85 · see citing works
A New Pest Detection Method Based on Improved YOLOv5m
Dai, M., Dorjoy, M. M. H., Miao, H., & Zhang, S. (2023) · Insects · doi:10.3390/insects14010054 · Cited by 85 · see citing works
Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications
Kawaharazuka, K., Oh, J., Yamada, J., Posner, I., & Zhu, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3609980 · Cited by 95 · see citing works
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning
Du, B., Gao, X., Hu, W., & Li, X. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475458 · Cited by 82 · see citing works
Emerging trends in autonomous vehicle perception: Multimodal fusion for 3D object detection
Alaba, S. Y., Gurbuz, A. C., & Ball, J. E. (2024) · World Electric Vehicle Journal · doi:10.3390/wevj15010020 · Cited by 82 · see citing works
Liu, J., Xu, L., & Chen, N. (2022) · Journal of Hydrology · doi:10.1016/j.jhydrol.2022.127748 · Cited by 79 · see citing works
A review of non-rigid transformations and learning-based 3D point cloud registration methods
Monji-Azad, S., Hesser, J., & Löw, N. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.12.023 · Cited by 81 · see citing works
- Authors
- Guo, M.-H., Cai, J.-X., Liu, Z.-N., Mu, T.-J., Martin, R. R., & Hu, S.-M.
- Affiliations
- Tsinghua University (CN)
- Cardiff University (GB)
- Venue
- Computational Visual Media
- Publisher
- Tsinghua University Press
- Published
- 2021-06
- Volume
- 7
- Issue
- 2
- Pages
- 187-199
- ISSN
- 2096-0433 (ISSN-L)
- 2096-0662
- OpenAlex
- W3111535274
- Cited by
- 2,100
- References
- 17
- Citations by year
- 2026: 309 · 2025: 505 · 2024: 515 · 2023: 428 · 2022: 266 · 2021: 76 · 2020: 1
- FWCI
- 182.77
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Computational Visual Media (publishedVersion, cc-by)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Deutsche Forschungsgemeinschaft
- National Natural Science Foundation of China (61761136018, 61521002)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- point cloud processing, point cloud transformer, transformer models, permutation invariance, farthest point sampling, nearest neighbor search, shape classification, part segmentation, semantic segmentation, surface normal estimation
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