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Cited by 2,1003D Shape Modeling and AnalysisAdvanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

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PCT: Point cloud transformer

Open access
Guo, Meng-Hao, Cai, Jun-Xiong, Liu, Zheng-Ning, Mu, Tai-Jiang, Martin, Ralph R., Hu, Shi-Min·2021·Computational Visual Media
DOI 10.1007/s41095-021-0229-5
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Abstract

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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Crossref · 10.1007/s41095-021-0229-5

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Figures

Fig. 2 PCT architecture. The encoder mainly comprises an Input Embedding module and four stacked Attention module. The decoder mainly comprises multiple Linear layers. Numbers above each module indicate its output channels. MA-Pool concatenates Max-Pool and Average-Pool . LBR combines Linear , BatchNorm , and ReLU layers. LBRD means LBR followed by a Dropout layer.
Fig. 2 PCT architecture. The encoder mainly comprises an Input Embedding module and four stacked Attention module. The decoder mainly comprises multiple Linear layers. Numbers above each module indicate its output channels. MA-Pool concatenates Max-Pool and Average-Pool . LBR combines Linear , BatchNorm , and ReLU layers. LBRD means LBR followed by a Dropout layer.CC-BY · From the PDF
Image 2
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Fig. 4 Left: Neighbor Embedding architecture. Middle: SG module with N in input points, d in input channels, k neighbors, N out output sampled points, and d out output channels. Top-right: example of sampling (colored balls represent sampled points). Bottom-right: example of grouping with k -NN neighbors. Number above LBR: number of output channels. Number above SG: number of sampled points and its output channels.
Fig. 4 Left: Neighbor Embedding architecture. Middle: SG module with N in input points, d in input channels, k neighbors, N out output sampled points, and d out output channels. Top-right: example of sampling (colored balls represent sampled points). Bottom-right: example of grouping with k -NN neighbors. Number above LBR: number of output channels. Number above SG: number of sampled points and its output channels.CC-BY · From the PDF
Fig. 5 Segmentations from PointNet, NPCT, SPCT, PCT, and ground truth (GT).
Fig. 5 Segmentations from PointNet, NPCT, SPCT, PCT, and ground truth (GT).CC-BY · From the PDF
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Image 6
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Image 7
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Image 8
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Citations

Cites

Show all 17 references

Research works printed but not linked: 20

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. Klokov, R.; Lempitsky, V. Escape from cells: Deep kdnetworks for the recognition of 3D point cloud models. In: Proceeding of the IEEE International Conference on Computer Vision, 863–872, 2017.
  17. 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.
  18. 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.
  19. 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.
  20. 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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Metadata

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
DOI
10.1007/s41095-021-0229-5
OpenAlex
W3111535274
Cited by
2,100
References
17
Citations by year
2026: 309 · 2025: 505 · 2024: 515 · 2023: 428 · 2022: 266 · 2021: 76
FWCI
182.77
Type
journal article
Language
en
Open access
Licence
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 › Computer Science › Computer Graphics and Computer-Aided Design › 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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