PR-Alignment: Multidimensional Adaptive Registration Algorithm Based on Practical Application Scenarios
In the present day, 3D point clouds are considered to be an important form of representing the 3D world. In computer vision, mobile robotics, and computer graphics, point cloud registration is a basic task, and it is widely used in 3D reconstruction, reverse engineering, among other applications. Ho...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/11/2/254 |
| _version_ | 1797619782168608768 |
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| author | Wenxin Wang Changming Zhao Haiyang Zhang |
| author_facet | Wenxin Wang Changming Zhao Haiyang Zhang |
| author_sort | Wenxin Wang |
| collection | DOAJ |
| description | In the present day, 3D point clouds are considered to be an important form of representing the 3D world. In computer vision, mobile robotics, and computer graphics, point cloud registration is a basic task, and it is widely used in 3D reconstruction, reverse engineering, among other applications. However, the mainstream method of point cloud registration is subject to the problems of a long registration time as well as a poor modeling effect, and these two factors cannot be balanced. To address this issue, we propose an adaptive registration mechanism based on a multi-dimensional analysis of practical application scenarios. Through the use of laser point clouds and RGB images, we are able to obtain geometric and photometric information, thus improving the data dimension. By adding target scene classification information to the RANSAC algorithm, combined with geometric matching and photometric matching, we are able to complete the adaptive estimation of the transformation matrix. We demonstrate via extensive experiments that our method achieves a state-of-the-art performance in terms of point cloud registration accuracy and time compared with other mainstream algorithms, striking a balance between expected performance and time cost. |
| first_indexed | 2024-03-11T08:31:44Z |
| format | Article |
| id | doaj.art-ab41f07665d343649804a82225a39d90 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-11T08:31:44Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-ab41f07665d343649804a82225a39d902023-11-16T21:46:01ZengMDPI AGMachines2075-17022023-02-0111225410.3390/machines11020254PR-Alignment: Multidimensional Adaptive Registration Algorithm Based on Practical Application ScenariosWenxin Wang0Changming Zhao1Haiyang Zhang2Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education, Beijing 100081, ChinaKey Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education, Beijing 100081, ChinaKey Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education, Beijing 100081, ChinaIn the present day, 3D point clouds are considered to be an important form of representing the 3D world. In computer vision, mobile robotics, and computer graphics, point cloud registration is a basic task, and it is widely used in 3D reconstruction, reverse engineering, among other applications. However, the mainstream method of point cloud registration is subject to the problems of a long registration time as well as a poor modeling effect, and these two factors cannot be balanced. To address this issue, we propose an adaptive registration mechanism based on a multi-dimensional analysis of practical application scenarios. Through the use of laser point clouds and RGB images, we are able to obtain geometric and photometric information, thus improving the data dimension. By adding target scene classification information to the RANSAC algorithm, combined with geometric matching and photometric matching, we are able to complete the adaptive estimation of the transformation matrix. We demonstrate via extensive experiments that our method achieves a state-of-the-art performance in terms of point cloud registration accuracy and time compared with other mainstream algorithms, striking a balance between expected performance and time cost.https://www.mdpi.com/2075-1702/11/2/254point cloudadaptive registration algorithmgeometric informationphotometric informationtransformation matrix |
| spellingShingle | Wenxin Wang Changming Zhao Haiyang Zhang PR-Alignment: Multidimensional Adaptive Registration Algorithm Based on Practical Application Scenarios Machines point cloud adaptive registration algorithm geometric information photometric information transformation matrix |
| title | PR-Alignment: Multidimensional Adaptive Registration Algorithm Based on Practical Application Scenarios |
| title_full | PR-Alignment: Multidimensional Adaptive Registration Algorithm Based on Practical Application Scenarios |
| title_fullStr | PR-Alignment: Multidimensional Adaptive Registration Algorithm Based on Practical Application Scenarios |
| title_full_unstemmed | PR-Alignment: Multidimensional Adaptive Registration Algorithm Based on Practical Application Scenarios |
| title_short | PR-Alignment: Multidimensional Adaptive Registration Algorithm Based on Practical Application Scenarios |
| title_sort | pr alignment multidimensional adaptive registration algorithm based on practical application scenarios |
| topic | point cloud adaptive registration algorithm geometric information photometric information transformation matrix |
| url | https://www.mdpi.com/2075-1702/11/2/254 |
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