EANet: A Point Cloud Registration Network Based on EdgeConv with Dual Attention Mechanism

DOI: https://doi.org/10.62517/jike.202304108

Author(s)

Chunrui Yang, Juan Zhu*, Xiaofeng Yue, Guolv Zhu

Affiliation(s)

School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun, Jilin, China *Corresponding Author

Abstract

In machine vision, point cloud registration is one of the core elements, which has been applied to many fields such as robot localisation, medical image processing, and autonomous driving. The main problem solved by point cloud registration is to solve the rotation matrix and translation vectors from one point cloud to another. This paper proposes a point cloud registration network based on EdgeConv with spatial attention mechanism. EdgeConv can dynamically construct graph structure and build topological relationships within the point cloud, so that each point can obtain multi-level feature representation; the attention mechanism can capture contextual information and improve the accuracy of the registration. Experimental results show that EANet has higher registration accuracy, stronger generalisation ability and robustness compared to ICP, Go ICP, FGR, PCRNet and PointNetLK.

Keywords

3D Point Cloud Registration; Deep Learning; EdgeConv; Attention Mechanism; Machine Vision

References

[1] Aoki, Y., Goforth, H., Srivatsan, R. A., & 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). [2] Sarode, V., Li, X., Goforth, H., Aoki, Y., Srivatsan, R. A., Lucey, S., & Choset, H. (2019). Pcrnet: Point cloud registration network using pointnet encoding. arXiv preprint arXiv:1908.07906. [3] Wang, Y., & Solomon, J. M. (2019). Deep closest point: Learning representations for point cloud registration. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 3523-3532). [4] Yang, J., Li, H., Campbell, D., & Jia, Y. (2015). Go-ICP: A globally optimal solution to 3D ICP point-set registration. IEEE transactions on pattern analysis and machine intelligence, 38(11), 2241-2254. [5] Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M. (2019). Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics (tog), 38(5), 1-12.