Point Cloud Registration Network Based on Convolution Fusion and Attention Mechanism | Neural Processing Letters Skip to main content
Springer Nature Link
Log in

Point Cloud Registration Network Based on Convolution Fusion and Attention Mechanism

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

In 3D vision, point cloud registration remains a major challenge, especially in end-to-end deep learning, where low-quality point pairs will directly lead to the degradation of registration accuracy. Therefore, we propose a point cloud registration network based on convolution fusion and a new attention mechanism to obtain high-quality point pairs and improve the accuracy of registration. In this work, we first fuse kernel point convolution and adaptive point convolution by cross-attention mechanism as the feature extraction backbone of the network to obtain features. Secondly, we use transformer to exchange information between source and target point clouds, which consists of a new attention mechanism module, named ReSE-Attention. It obtains a global feature view by adding a squeeze extraction module and deep learnable parameters to the normal attention mechanism. And then, a regression decoder is adapted to generate the correct point pairs. Finally, we first introduce Focal Loss on the loss function in point cloud registration to balance the relationship between overlapping and non-overlapping regions. Our approach is evaluated on both the scene dataset 3DMatch and the object dataset ModelNet and achieves state-of-the-art performance.

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
¥17,985 /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 includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Explore related subjects

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

Availability of Data and Materials

The datasets generated during the current study are available from the corresponding author upon reasonable request.

References

  1. Huang X, Mei G, Zhang J, Abbas R (2021) A comprehensive survey on point cloud registration. arXiv preprint arXiv:2103.02690

  2. Takimoto R Y, Tsuzuki MdSG, Vogelaar R, Castro Martins T, Sato A K, Iwao Y, Gotoh T, Kagei S (2016) 3d reconstruction and multiple point cloud registration using a low precision RGB-D sensor. Mechatronics 35:11–22

    Article  Google Scholar 

  3. Dang Z, Wang L, Guo Y, Salzmann M (2022) Learning-based point cloud registration for 6d object pose estimation in the real world. In: European conference on computer vision, pp. 19– 37 . Springer

  4. Choy C, Park J, Koltun V (2019) Fully convolutional geometric features. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 8958– 8966

  5. Zeng A, Song S, Nießner M, Fisher M, Xiao J, Funkhouser T (2017) 3dmatch: learning local geometric descriptors from RGB-D reconstructions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1802– 1811

  6. Deng H, Birdal T, Ilic S (2018) Ppfnet: global context aware local features for robust 3d point matching. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 195– 205

  7. Bai X, Luo Z, Zhou L, Fu H, Quan L, Tai C-L (2020) D3feat: joint learning of dense detection and description of 3d local features. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 6359– 6367

  8. Yew ZJ, Lee GH (2018) 3dfeat-net: weakly supervised local 3d features for point cloud registration. In: Proceedings of the european conference on computer vision (ECCV), pp. 607– 623

  9. Huang S, Gojcic Z, Usvyatsov M, Wieser A, Schindler K (2021) Predator: registration of 3d point clouds with low overlap. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 4267– 4276

  10. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

  11. Deng H, Birdal T, Ilic S (2018) Ppf-foldnet: unsupervised learning of rotation invariant 3d local descriptors. In: Proceedings of the European conference on computer vision (ECCV), pp. 602– 618

  12. Gojcic Z, Zhou C, Wegner JD, Wieser A (2019) The perfect match: 3d point cloud matching with smoothed densities. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 5545– 5554

  13. Shi S, Wang X, Li H (2019) Pointrcnn: 3d object proposal generation and detection from point cloud. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 770– 779

  14. Hu Q , Yang B, Xie L, Rosa S, Guo Y, Wang Z, Trigoni N, Markham A (2020) Randla-net: efficient semantic segmentation of large-scale point clouds. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 11108– 11117

  15. Wang Y, Sun Y, Liu Z, Sarma SE, Bronstein MM, Solomon JM (2019) Dynamic graph CNN for learning on point clouds. ACM Trans Gr (TOG) 38(5):1–12

    Article  Google Scholar 

  16. Qi CR, Yi L, Su H, Guibas LJ (2017) Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in neural information processing systems 30

  17. Li Y, Bu R, Sun M, Wu W, Di X, Chen B (2018) Pointcnn: convolution on x-transformed points. In: Advances in neural information processing systems 31

  18. Wu W, Qi Z, Fuxin L (2019) Pointconv: deep convolutional networks on 3d point clouds. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 9621– 9630

  19. Xu Q, Sun X, Wu C-Y, Wang P, Neumann U (2020) Grid-gcn for fast and scalable point cloud learning. in: proceedings of the ieee/cvf Conference on Computer Vision and Pattern Recognition, pp. 5661– 5670

  20. Zhou H, Feng Y, Fang M, Wei M, Qin J, Lu T (2021) Adaptive graph convolution for point cloud analysis. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 4965– 4974

  21. Yew ZJ, Lee GH (2022) Regtr: end-to-end point cloud correspondences with transformers. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 6677– 6686

  22. Qin Z, Yu H, Wang C, Guo Y, Peng Y, Xu K (2022) Geometric transformer for fast and robust point cloud registration. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 11143– 11152

  23. Zhou D, Kang B, Jin X, Yang L, Lian X, Jiang Z, Hou Q, Feng J (2021) Deepvit: towards deeper vision transformer. arXiv preprint arXiv:2103.11886

  24. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132– 7141

  25. Lin T-Y, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp. 2980– 2988

  26. Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X, Xiao J (2015) 3d shapenets: a deep representation for volumetric shapes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1912– 1920

  27. Besl PJ, McKay ND (1992) Method for registration of 3-d shapes. In: Sensor Fusion IV: control paradigms and data structures, vol. 1611, pp. 586– 606. Spie

  28. Aiger D, Mitra NJ, Cohen-Or D (2008) 4-points congruent sets for robust pairwise surface registration. In: ACM SIGGRAPH 2008 papers, pp. 1– 10

  29. Rusu RB, Blodow N, Marton ZC, Beetz M (2008) Aligning point cloud views using persistent feature histograms. In: 2008 IEEE/RSJ international conference on intelligent robots and systems, pp. 3384– 3391 . IEEE

  30. Rusu RB, Blodow N, Beetz M (2009) Fast point feature histograms (FPFH) for 3d registration. In: 2009 IEEE international conference on robotics and automation, pp. 3212– 3217. IEEE

  31. Tombari F, Salti S, Di Stefano L (2010) Unique shape context for 3d data description. In: Proceedings of the ACM workshop on 3D object retrieval, pp. 57– 62

  32. Chen H, Bhanu B (2007) 3d free-form object recognition in range images using local surface patches. Pattern Recogn Lett 28(10):1252–1262

    Article  Google Scholar 

  33. Salti S, Tombari F, Di Stefano L (2014) Shot: unique signatures of histograms for surface and texture description. Comput Vis Image Underst 125:251–264

    Article  Google Scholar 

  34. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431– 3440

  35. Yu H, Li F, Saleh M, Busam B, Ilic S (2021) Cofinet: reliable coarse-to-fine correspondences for robust pointcloud registration. Adv Neural Inf Process Syst 34:23872–23884

    Google Scholar 

  36. Li Y, Harada T (2022) Lepard: learning partial point cloud matching in rigid and deformable scenes. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 5554– 5564

  37. Wang Y, Solomon JM (2019) Deep closest point: learning representations for point cloud registration. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 3523– 3532

  38. Cao A-Q, Puy G, Boulch A, Marlet R (2021) Pcam: product of cross-attention matrices for rigid registration of point clouds. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 13229– 13238

  39. Yuan W, Eckart B, Kim K, Jampani V, Fox D, Kautz J (2020) Deepgmr: learning latent gaussian mixture models for registration. In: Computer vision–ECCV 2020: 16th European conference, Glasgow, Proceedings, Part V 16, pp. 733– 750. Springer

  40. Aoki Y, Goforth H, Srivatsan RA, Lucey S (2019) Pointnetlk: robust & efficient point cloud registration using pointnet. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 7163– 7172

  41. Baker S, Matthews I (2004) Lucas-kanade 20 years on: a unifying framework. Int J Comput Vis 56:221–255

    Article  Google Scholar 

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

  43. Graham B, Engelcke M, Van Der Maaten L (2018) 3d semantic segmentation with submanifold sparse convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 9224– 9232

  44. Qi CR, Su H, Mo K, Guibas LJ (2017) Pointnet: deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 652– 660

  45. Pais GD, Ramalingam S, Govindu VM, Nascimento JC, Chellappa R, Miraldo P (2020) 3dregnet: a deep neural network for 3d point registration. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 7193– 7203

  46. Lee J, Kim S, Cho M, Park J (2021) Deep hough voting for robust global registration. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 15994– 16003

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

  48. Yi KM, Trulls E, Ono Y, Lepetit V, Salzmann M, Fua P (2018) Learning to find good correspondences. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2666–2674

  49. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems 30

  50. Qiu S, Anwar S, Barnes N (2022) Pu-transformer: point cloud upsampling transformer. In: Proceedings of the Asian conference on computer vision, pp. 2475– 2493

  51. Yang J, Zhang Q, Ni B, Li L, Liu J, Zhou M, Tian Q (2019) Modeling point clouds with self-attention and gumbel subset sampling. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 3323– 3332

  52. He C, Li R, Li S, Zhang L (2022) Voxel set transformer: a set-to-set approach to 3d object detection from point clouds. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 8417– 8427

  53. Thomas H, Qi CR, Deschaud J-E, Marcotegui B, Goulette F, Guibas LJ (2019) Kpconv: flexible and deformable convolution for point clouds. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 6411– 6420

  54. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, pp. 448– 456. pmlr

  55. Xu B, Wang N, Chen T, Li M (2015) Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853

  56. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770– 778

  57. Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp. 315– 323 . JMLR workshop and conference proceedings

  58. Kabsch W (1976) A solution for the best rotation to relate two sets of vectors. Acta Crystallogr Sect A Cryst Phys Diffr Theor Gen Crystallogr 32(5):922–923

    Article  Google Scholar 

  59. Umeyama S (1991) Least-squares estimation of transformation parameters between two point patterns. IEEE Trans Pattern Anal Mach Intell 13(04):376–380

    Article  Google Scholar 

  60. Oord A, Li Y, Vinyals O (2018) Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748

  61. Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101

  62. Xu H, Liu S, Wang G, Liu G, Zeng B (2021) Omnet: learning overlapping mask for partial-to-partial point cloud registration. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 3132– 3141

  63. Yew ZJ, Lee GH (2020) Rpm-net: robust point matching using learned features. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 11824–11833

Download references

Acknowledgements

This work was supported by the Zhejiang Provincial Natural Science Foundation of China under Grants for Research on Dynamic 3D Point Cloud Compression Method for Holographic Video Transmission (LY21F010009) and Research on Performance Degradation and Life Prediction of Traction System under Mixed Uncertainty (LQ23F030016).

Author information

Authors and Affiliations

  1. College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China

    Wei Zhu, Yue Ying, Jin Zhang, Xiuli Wang & Yayu Zheng

Authors
  1. Wei Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yue Ying
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiuli Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yayu Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, project administration, formal analysis, and funding are provided by WZ. Methodology, software, investigation writing—original draft and visualization are performed by YY. Validation, resources, writing review, and editing are provided by JZ. Writing review, editing, and funding are provided by XW. The paper is reviewed and edited by YZ. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yayu Zheng.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, W., Ying, Y., Zhang, J. et al. Point Cloud Registration Network Based on Convolution Fusion and Attention Mechanism. Neural Process Lett 55, 12625–12645 (2023). https://doi.org/10.1007/s11063-023-11435-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-023-11435-6

Keywords

Search

Navigation