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WU Zhi-hui, WANG Li-zhong, LIANG Jin, GONG Chun-yuan, ZHU Feng, CHANG Zhi-wen, XU Jian-ning. Segmentation method for enhanced features in automatic registration of triangular mesh model of mechanical parts[J]. Chinese Optics. doi: 10.37188/CO.2023-0225
Citation: WU Zhi-hui, WANG Li-zhong, LIANG Jin, GONG Chun-yuan, ZHU Feng, CHANG Zhi-wen, XU Jian-ning. Segmentation method for enhanced features in automatic registration of triangular mesh model of mechanical parts[J]. Chinese Optics. doi: 10.37188/CO.2023-0225

Segmentation method for enhanced features in automatic registration of triangular mesh model of mechanical parts

doi: 10.37188/CO.2023-0225
Funds:  Supported by the National Key R&D Program of China (No. 2022YFB4601802); National Natural Science Foundation of China (No. 52275543)
More Information
  • Corresponding author: wanglz@mail.xjtu.edu.cn
  • Received Date: 18 Dec 2023
  • Accepted Date: 28 Feb 2024
  • Available Online: 17 May 2024
  • Triangular mesh model registration is an important part of industrial automation detection software. The registration accuracy has an important influence on the shape and position tolerance of mechanical parts. Aiming at the problems of low accuracy and poor robustness of automatic registration of triangular mesh models, this paper proposes a segmentation method for enhanced features in automatic registration of triangular mesh models for mechanical parts. Firstly, the K value of the feature segmentation of the triangular mesh model is determined, and the seed points are determined by the Laplacian matrix for iterative initialization. Secondly, this paper uses the appropriate region shape agent and cost function to accelerate the process, and performs multi-source iterative clustering to obtain the feature segmentation results. Finally, based on the feature segmentation results of the triangular mesh model, the coarse registration based on the singular value decomposition method is performed, and the fine registration is performed according to the EM-ICP. Compared with the traditional feature descriptor coarse registration and ICP fine registration method, the experimental results show that the registration error of the proposed method is reduced by 25.2 %, and the automatic registration time is shortened by 62.6 %, which effectively improves the accuracy and efficiency of the automatic registration of the triangular mesh model.


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