Multiview Point Cloud Registration Based on Minimum Potential Energy for Free-Form Blade Measurement
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
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| _version_ | 1866913686268936192 |
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| author | Wu, Zijie Wang, Yaonan Mo, Yang Zhu, Qing Xie, He Wu, Haotian Feng, Mingtao Mian, Ajmal |
| author_facet | Wu, Zijie Wang, Yaonan Mo, Yang Zhu, Qing Xie, He Wu, Haotian Feng, Mingtao Mian, Ajmal |
| contents | Point cloud registration is an essential step for free-form blade reconstruction in industrial measurement. Nonetheless, measuring defects of the 3D acquisition system unavoidably result in noisy and incomplete point cloud data, which renders efficient and accurate registration challenging. In this paper, we propose a novel global registration method that is based on the minimum potential energy (MPE) method to address these problems. The basic strategy is that the objective function is defined as the minimum potential energy optimization function of the physical registration system. The function distributes more weight to the majority of inlier points and less weight to the noise and outliers, which essentially reduces the influence of perturbations in the mathematical formulation. We decompose the solution into a globally optimal approximation procedure and a fine registration process with the trimmed iterative closest point algorithm to boost convergence. The approximation procedure consists of two main steps. First, according to the construction of the force traction operator, we can simply compute the position of the potential energy minimum. Second, to find the MPE point, we propose a new theory that employs two flags to observe the status of the registration procedure. We demonstrate the performance of the proposed algorithm on four types of blades. The proposed method outperforms the other global methods in terms of both accuracy and noise resistance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07680 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multiview Point Cloud Registration Based on Minimum Potential Energy for Free-Form Blade Measurement Wu, Zijie Wang, Yaonan Mo, Yang Zhu, Qing Xie, He Wu, Haotian Feng, Mingtao Mian, Ajmal Computer Vision and Pattern Recognition Computational Geometry Point cloud registration is an essential step for free-form blade reconstruction in industrial measurement. Nonetheless, measuring defects of the 3D acquisition system unavoidably result in noisy and incomplete point cloud data, which renders efficient and accurate registration challenging. In this paper, we propose a novel global registration method that is based on the minimum potential energy (MPE) method to address these problems. The basic strategy is that the objective function is defined as the minimum potential energy optimization function of the physical registration system. The function distributes more weight to the majority of inlier points and less weight to the noise and outliers, which essentially reduces the influence of perturbations in the mathematical formulation. We decompose the solution into a globally optimal approximation procedure and a fine registration process with the trimmed iterative closest point algorithm to boost convergence. The approximation procedure consists of two main steps. First, according to the construction of the force traction operator, we can simply compute the position of the potential energy minimum. Second, to find the MPE point, we propose a new theory that employs two flags to observe the status of the registration procedure. We demonstrate the performance of the proposed algorithm on four types of blades. The proposed method outperforms the other global methods in terms of both accuracy and noise resistance. |
| title | Multiview Point Cloud Registration Based on Minimum Potential Energy for Free-Form Blade Measurement |
| topic | Computer Vision and Pattern Recognition Computational Geometry |
| url | https://arxiv.org/abs/2502.07680 |