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| Main Author: | |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.08587 |
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| _version_ | 1866914642971852800 |
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| author | Cheng, Jia |
| author_facet | Cheng, Jia |
| contents | In order to solve the problem of point cloud data splitting improved by DPC algorithm, a research on automatic separation and 3D reconstruction of point cloud data split lines is proposed. First, the relative coordinates of each point in the cloud point are calculated. Second, it is planned to develop a relative ensemble-based DPC swarm algorithm for analyzing the number of separation lines to determine all parts in the cloud content. Finally, fit each separator using the least squares method. iron. The cloud point of the resulting split subconductors has a clear demarcation line, and the distance between adjacent split subconductors is 0.45 m, divided by the four vertices of the square. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_08587 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Automatic extraction and 3D reconstruction of split wire from point cloud data based on improved DPC algorithm Cheng, Jia Computer Vision and Pattern Recognition Artificial Intelligence In order to solve the problem of point cloud data splitting improved by DPC algorithm, a research on automatic separation and 3D reconstruction of point cloud data split lines is proposed. First, the relative coordinates of each point in the cloud point are calculated. Second, it is planned to develop a relative ensemble-based DPC swarm algorithm for analyzing the number of separation lines to determine all parts in the cloud content. Finally, fit each separator using the least squares method. iron. The cloud point of the resulting split subconductors has a clear demarcation line, and the distance between adjacent split subconductors is 0.45 m, divided by the four vertices of the square. |
| title | Automatic extraction and 3D reconstruction of split wire from point cloud data based on improved DPC algorithm |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2401.08587 |