Abstract
Global feature extraction and rigid body transformation estimation are two key steps in correspondences-free point cloud registration methods. Previous approaches only utilize the global information while the local information is ignored. Moreover, global and local information may play different roles on multiple point clouds. In this paper, we verify the sensitivity of different types of point clouds to global and local information. We conducted extensive experiments on the ModelNet40 dataset by the SGLF-DQNet. Through the experimental results, we summarize the point cloud structure of the sensitivity to global and local features in the correspondence-free point cloud registration task.
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References
Jost, T., Hugli, H.: A multi-resolution scheme ICP algorithm for fast shape registration. In: International Symposium on 3D Data Processing Visualization & Transmission (2008)
Wang, Q., Kim, M.-K.: Applications of 3D point cloud data in the construction industry: a fifteen-year review from 2004 to 2018. Adv. Eng. Inform. 39, 306–319 (2019)
Wu, Y., et al.: Computational intelligence in remote sensing image registration: a survey. Int. J. Autom. Comput. 18(1), 17 (2021)
Ma, W., et al.: Feature split-merge-enhancement network for remote sensing object detection. IEEE Trans. Geosci. Remote Sens. 60, 1–17 (2022)
Segal, A., Haehnel, D., Thrun, S.: Generalized-ICP. In: Robotics: Science and Systems (2009)
Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 3212–3217 (2009)
Wang, Y., Solomon, J.M.: PRNet: self-supervised learning for partial-to-partial registration (2019)
Yew, Z.J., Lee, G.H.: 3DFeat-Net: weakly supervised local 3D features for point cloud registration. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 630–646. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01267-0_37
Bouaziz, S., Tagliasacchi, A., Pauly, M.: Sparse iterative closest point. In: Computer Graphics Forum, pp. 113–123 (2013)
Pöppelbaum, J., Schwung, A.: Predicting rigid body dynamics using dual quaternion recurrent neural networks with quaternion attention. arXiv preprint arXiv:2011.08734 (2020)
Nikhil, G.N., Meraz, M., Javed, M.: Automatic on-road object detection in LiDAR-point cloud data using modified VoxelNet architecture. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds.) CVIP 2020. CCIS, vol. 1378, pp. 201–213. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-1103-2_18
Li, Y., Bu, R., Sun, M., Chen, B.: PointCNN (2018)
Sarode, V., et al.: One framework to register them all: PointNet encoding for point cloud alignment. arXiv preprint arXiv:1912.05766 (2019)
Ma, W., et al.: A collaborative correlation-matching network for multimodality remote sensing image classification. IEEE Trans. Geosci. Remote Sens. 60, 1–18 (2022)
Wu, Y., et al.: Feature mining method of multi-dimensional information fusion in point cloud registration. J. Comput. Res. Dev. (59) (2022)
Kenwright, B.: A beginners guide to dual-quaternions: what they are, how they work, and how to use them for 3D character hierarchies. In: Proceedings of International Conference on Computer Graphics, Visualization and Computer Vision, pp. 1–13 (2012)
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Ma, W., Yue, M., Yuan, Y., Wu, Y., zhu, H., Jiao, L. (2022). Point Cloud Registration Based on Global and Local Feature Fusion. In: Shi, Z., Jin, Y., Zhang, X. (eds) Intelligence Science IV. ICIS 2022. IFIP Advances in Information and Communication Technology, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-031-14903-0_33
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DOI: https://doi.org/10.1007/978-3-031-14903-0_33
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