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Probability re-weighted 3D point cloud registration for missing correspondences

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Abstract

Rigid 3D point cloud registration is a challenging problem due to noise, outliers, variations in initial positions, and limited amounts of overlap. Existing methods lack a simple mechanism to deal with missing correspondence and usually fail to align point clouds in the presence of massive outliers and missing correspondences. Aiming at the problem of outliers and missing correspondences in the partially overlapping point cloud, a probability re-weighted 3D point cloud registration algorithm based on the Gaussian mixture model (GMM) is proposed in this paper. Firstly, the correspondences between the 3D target and source point clouds are established by the GMM and uniform distribution. We show that the missing correspondences in the target point cloud can be handled by re-weighting the mixing proportion of GMM through a prior probability re-weighting strategy. Secondly, we propose a posteriori probability inference strategy to infer the outliers and their proportion in the source point cloud, where the potential outliers are removed when solving the GMM parameters. Thirdly, the objective function in the form of point-to-plane distance is introduced by calculating the normal direction in the vicinity of the weight-averaged target point, and then the point clouds with large plane structures are registered finely. Finally, the experiments are conducted on Stanford 3D Scanning data and real 3D scene data. The overall RMSE on the former is 0.40 mm with fitness of 0.775, and it is 5.32 mm with fitness of 0.608 on the latter. The evaluation results show that the proposed algorithm can enhance the fitness and reduce the RMSE of the rigid 3D point cloud registration and improve the accuracy of registration.

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Acknowledgements

This work was supported partially by the National Natural Science Foundation of China (No.51875152), the Graduate Students Education Innovation Foundation of Shanxi Province (No.2021Y698) and the Natural Science Foundation of Shanxi Province (No.201801D121134).

Funding

This work was supported partially by the National Natural Science Foundation of China (No.51875152), the Graduate Students Education Innovation Foundation of Shanxi Province (No.2021Y698) and the Natural Science Foundation of Shanxi Province (No.201801D121134).

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Authors and Affiliations

  1. School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, 030024, China

    Zhiliang Sun, Rongguo Zhang & Jing Hu

  2. School of Mechanical Engineering, Hefei University of Technology, Hefei, 230009, China

    Xiaojun Liu

Authors
  1. Zhiliang Sun
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  2. Rongguo Zhang
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  3. Jing Hu
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  4. Xiaojun Liu
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Contributions

Zhiliang Sun: Conceptualization, Methodology, Software, Data curation, Validation, Visualization, Writing-Original draft preparation.

Rongguo Zhang: Project administration, Supervision, Writing-Reviewing and Editing, Investigation, Funding acquisition.

Jing Hu: Formal analysis, Software, Supervision.

Xiaojun Liu: Methodology, Funding acquisition.

Corresponding author

Correspondence to Rongguo Zhang.

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Sun, Z., Zhang, R., Hu, J. et al. Probability re-weighted 3D point cloud registration for missing correspondences. Multimed Tools Appl 81, 11107–11126 (2022). https://doi.org/10.1007/s11042-022-12134-5

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  • DOI: https://doi.org/10.1007/s11042-022-12134-5

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