Open Access

ARTICLE

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

Yan Zhu^1,2, Lili Tian², Fan Ye², Gaofeng Sun¹, Xianyong Fang^2,*

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:

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

Computer Modeling in Engineering & Sciences 2023, 136(2), 1835-1856. https://doi.org/10.32604/cmes.2023.025662

Received 25 July 2022; Accepted 08 October 2022; Issue published 06 February 2023

Abstract

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.

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Keywords

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