Skip to main content

Advertisement

Springer Nature Link
Log in

A novel partial point cloud registration method based on graph attention network

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Point cloud registration is a challenging task due to sparsity and unknown initial correspondence information. The traditional registration methods tend to converge to local optimal solutions and rely on good initial correspondence information. Deep learning-based methods show good adaptability to initial information and noises, but they cannot effectively cope with partial-to-partial registration scenes. This paper proposes a partial point cloud registration method based on graph attention network. The context information of the point cloud is obtained by a message passing mechanism. The attention features of the key registration points are extracted by an attention network. The key matching points are chosen by a key point selection module. Virtual correspondences are generated based on these key points and their features. A rigid transformation is obtained based on the virtual registration by a singular value decomposition layer. The performance of the proposed method is evaluated in three scenarios based on the ModelNet40 dataset. Experimental results show that the proposed method is robust to arbitrary initial positions and noises. It obtains higher registration accuracy than traditional methods while maintaining low network complexity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Prieto, S.A., Adan, A., Quintana, B.: Preparation and enhancement of 3D laser scanner data for realistic coloured BIM models. Vis. Comput. 36(1), 113–126 (2020)

    Article  Google Scholar 

  2. Liu, T.R., Cai, Y.Y., Zheng, J.M., Thalmann, N.M.: BEACon: a boundary embedded attentional convolution network for point cloud instance segmentation. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02112-7

    Article  Google Scholar 

  3. Meng, H.-Y., Gao, L., Lai, Y.-K., Manocha, D., net Vv-: Voxel vae net with group convolutions for point cloud segmentation, In: Proceedings of IEEE/CVF International Conference on Computer Vision, pp. 8500–8508 (2019)

  4. Hu, L., Xiao, J., Wang, Y.: An automatic 3D registration method for rock mass point clouds based on plane detection and polygon matching. Vis. Comput. 36(4), 669–681 (2020)

    Article  MathSciNet  Google Scholar 

  5. Wang, C., Xu, Y.H., Wang, L., Li, C.M.: Fast structural global registration of indoor colored point cloud. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02295-z

    Article  Google Scholar 

  6. Dong, K., Gao, S.S., Xin, S.Q., Zhou, Y.F.: Probability driven approach for point cloud registration of indoor scene. Vis. Comput. (2020). https://doi.org/10.1007/s00371-020-01999-y

    Article  Google Scholar 

  7. Gojcic, C., Zhou, J.D., Wegner, L.J., Guibas, T., Birdal: Learning multiview 3d point cloud registration, In: Proceedings of IEEE/CVF conference on computer vision and pattern recognition, pp. 1759–1769 (2020)

  8. Choy, C., Dong, W., Koltun, V.: Deep global registration, In: Proceedings of IEEE/CVF conference on computer vision and pattern recognition, pp. 2514–2523 (2020)

  9. Lee, D., Hamsici, O.C., Feng, S., Sharma, P., Gernoth, T., DeepPRO: Deep partial point cloud registration of objects, In: Proceedings of IEEE/CVF International Conference on Computer Vision, pp. 5683–5692 (2021)

  10. Huang, S., Gojcic, Z., Usvyatsov, M., Wieser, A., Schindler, K., PREDATOR: Registration of 3D point clouds with low overlap, In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4267–4276 (2021)a

  11. Besl, P.J., Mckay, H.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

    Article  Google Scholar 

  12. Yang, J., Li, H., Campbell, D., Jia, Y.: Go-ICP: A globally optimal solution to 3D ICP point-set registration. IEEE Trans. Pattern Anal. Mach. Intell. 38(11), 2241–2254 (2016)

    Article  Google Scholar 

  13. Zhou, Q.-Y., Park, J., Koltun, V.: Fast global registration, In: Proceedings of European Conference on Computer Vision, pp. 766–782 (2016)

  14. Aoki, Y., Goforth, H., Srivatsan, R.A., Lucey, S., Pointnetlk: Robust & efficient point cloud registration using pointnet, In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 7163–7172 (2019)

  15. Charles, R.Q., Su, H., Mo, K., Guibas, L.J., PointNet: Deep learning on point sets for 3D classification and segmentation, In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 77–85 (2017)

  16. Wang, Y., Solomon, J.M.: Deep closest point: learning representations for point cloud registration, In: Proceedings of IEEE International Conference on Computer Vision, pp. 3523–3532 (2019)

  17. 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 Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

  18. Wang, Y., Solomon, J. PRNet: self-supervised learning for partial-to-partial registration, In: Proceedings of International Conference on Neural Information Processing Systems, pp. 8814–8826 (2019)

  19. Wei, H., Qiao, Z., Liu, Z., Suo, C., Yin, P., Shen, Y., Li, H., Wang, H.: End-to-End 3D Point cloud learning for registration task using virtual correspondences, In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2678–2683 (2020)

  20. Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3D ShapeNets: A deep representation for volumetric shapes, In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1912–1920 (2015)

  21. Rusinkiewicz, S.: A symmetric objective function for ICP. ACM Trans. Graphics 38(4), 1–7 (2019)

    Article  Google Scholar 

  22. Basdogan, C., Oztireli, A.C.: A new feature-based method for robust and efficient rigid-body registration of overlapping point clouds. Vis. Comput. 24(7), 679–688 (2008)

    Article  Google Scholar 

  23. Rosen, D.M., Carlone, L., Bandeira, A.S., Leonard, J.J.: SE-Sync: A certifiably correct algorithm for synchronization over the special euclidean group. Int. J. Robot. Res. 38(2–3), 95–125 (2019)

    Article  Google Scholar 

  24. Maron, H., Dym, N., Kezurer, I., Kovalsky, S., Lipman, Y.: Point registration via efficient convex relaxation. ACM Trans. Graphics 35(4), 1–12 (2016)

    Article  Google Scholar 

  25. Yang, H., Shi, J., Carlone, L.: Teaser: Fast and certifiable point cloud registration. IEEE Trans. Rob. 37(2), 314–333 (2020)

    Article  Google Scholar 

  26. 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 Advances in Neural Information Processing Systems, pp. 5099–5108 (2017)

  27. Atzmon, M., Maron, H., Lipman, Y.: Point convolutional neural networks by extension operators. ACM Trans. Graphics (TOG) 37(4), 1–12 (2018)

    Article  Google Scholar 

  28. Liu, Y., Fan, B., Meng, G., Lu, J., Xiang, S., Pan, C.: Densepoint: Learning densely contextual representation for efficient point cloud processing, In: Proceedings of IEEE/CVF International Conference on Computer Vision, pp. 5239–5248 (2019)

  29. Wang, Y., Sun, Y.B., Liu, Z.W., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graphics 38(5), 1–12 (2019)

    Article  Google Scholar 

  30. Sarode, V., Li, X., Goforth, H., Aoki, Y., Srivatsan, R.A., Lucey, S., Choset, H.: PCRNet: Point cloud registration network using PointNet encoding, arXiv preprint arXiv:1908.0790 (2019)

  31. Vinyals, O., Fortunato, M., Jaitly, N.: Pointer networks, In: Proceedings of Advances in Neural Information Processing Systems, pp. 2692–2700 (2015)

  32. Pais, G.D., Ramalingam, S., Govindu, V.M., Nascimento, J.C., Chellappa, R., Miraldo, P.: 3dregnet: A deep neural network for 3d point registration, In: Proceedings of IEEE/CVF conference on computer vision and pattern recognition, pp. 7193–7203 (2020)

  33. Li, J., Zhang, C., Xu, Z., Zhou, H., Zhang, C: Iterative distance-aware similarity matrix convolution with mutual-supervised point elimination for efficient point cloud registration, In: Proceedings of European Conference on Computer Vision, pp. 378–394 (2020)

  34. Yew, Z.J. Lee, G.H.: Rpm-net: Robust point matching using learned features, In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 11824–11833 (2020)

  35. Gold, S., Rangarajan, A., Lu, C.-P., Pappu, S., Mjolsness, E.: New algorithms for 2D and 3D point matching: pose estimation and correspondence. Pattern Recogn. 31(8), 1019–1031 (1998)

    Article  Google Scholar 

  36. Fu, K., Liu, S., Luo, X., Wang, M.: Robust point cloud registration framework based on deep graph matching, In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 8893–8902 (2021)

  37. Wu, B., Ma, J., Chen, G., An, P.: Feature interactive representation for point cloud registration, In: Proceedings of IEEE/CVF International Conference on Computer Vision, pp. 5530–5539 (2021).

  38. Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., Y, T.: Do transformers really perform bad for graph representation?, arXiv preprint arXiv:2106.05234 (2021)

Download references

Acknowledgements

This work was supported by China Postdoctoral Science Foundation [Grant Number 2021M692778].

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Yanan Song

  2. Institute of Computing Innovation, Zhejiang University, Hangzhou, China

    Yanan Song

  3. State Key Lab. of Digital Manufacturing Equipment & Technology, Huazhong University of Science & Technology, Wuhan, China

    Weiming Shen

  4. School of Management, Wuhan University of Science and Technology, Wuhan, China

    Kunkun Peng

Authors
  1. Yanan Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weiming Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kunkun Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weiming Shen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, Y., Shen, W. & Peng, K. A novel partial point cloud registration method based on graph attention network. Vis Comput 39, 1109–1120 (2023). https://doi.org/10.1007/s00371-021-02391-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-021-02391-0

Keywords

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy