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Automatic Extraction of the Sparse Prior Correspondences for Non-Rigid Point Cloud Registration

Yan Zhu1,2, Lili Tian2, Fan Ye2, Gaofeng Sun1, Xianyong Fang2,*

1 Department of Sports and Military Education, Anhui University, Hefei, 230601, China
2 School of Computer Science and Technology, Anhui University, Hefei, 230601, China

* Corresponding Author: Xianyong Fang. Email: email

(This article belongs to this Special Issue: Integration of Geometric Modeling and Numerical Simulation)

Computer Modeling in Engineering & Sciences 2023, 136(2), 1835-1856.


Non-rigid registration of point clouds is still far from stable, especially for the largely deformed one. Sparse initial correspondences are often adopted to facilitate the process. However, there are few studies on how to build them automatically. Therefore, in this paper, we propose a robust method to compute such priors automatically, where a global and local combined strategy is adopted. These priors in different degrees of deformation are obtained by the locally geometrical-consistent point matches from the globally structural-consistent region correspondences. To further utilize the matches, this paper also proposes a novel registration method based on the Coherent Point Drift framework. This method takes both the spatial proximity and local structural consistency of the priors as supervision of the registration process and thus obtains a robust alignment for clouds with significantly different deformations. Qualitative and quantitative experiments demonstrate the advantages of the proposed method.


Cite This Article

Zhu, Y., Tian, L., Ye, F., Sun, G., Fang, X. (2023). Automatic Extraction of the Sparse Prior Correspondences for Non-Rigid Point Cloud Registration. CMES-Computer Modeling in Engineering & Sciences, 136(2), 1835–1856.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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