Digital image correlation calculation method for RGB-D camera multi-view matching using variable template
•In single-camera multi-view matching, cumulative errors and mismatches are common challenges.•Variable template matching methods reduce cumulative errors and mismatches in multi-view scenarios.•Dynamic adjustment of template parameters, based on intermediate results, greatly enhances matching accur...
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| Vydané v: | Measurement : journal of the International Measurement Confederation Ročník 240; s. 115617 |
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30.01.2025
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| ISSN: | 0263-2241 |
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| Abstract | •In single-camera multi-view matching, cumulative errors and mismatches are common challenges.•Variable template matching methods reduce cumulative errors and mismatches in multi-view scenarios.•Dynamic adjustment of template parameters, based on intermediate results, greatly enhances matching accuracy.•Improved sub-pixel registration accuracy increases efficiency and convergence in multi-view point cloud registration.
Multi-view matching is an indispensable method for digital image correlation 3D reconstruction. However, as the camera viewing angle changes, traditional DIC methods can lead to the accumulation of matching errors and an increase in mismatches, thereby affecting the 3D reconstruction quality. This paper proposes a digital image correlation method for multi-view RGB-D images based on variable template matching. Initial positioning is achieved by dynamically adjusting the size and sampling direction of the variable template. Sub-pixel registration is then performed using the inverse compositional algorithm, followed by multi-view point cloud fusion using the Iterative Closest Point (ICP) registration method. Experimental results show that the iteration speed of point cloud registration is increased by 30%, and the cumulative error is reduced by 62.5%. These improvements demonstrate that the proposed method is suitable for multi-viewpoint matching scenarios, achieving excellent results in multi-view feature point matching and multi-view point cloud fusion, significantly enhancing 3D reconstruction accuracy and efficiency. |
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| AbstractList | •In single-camera multi-view matching, cumulative errors and mismatches are common challenges.•Variable template matching methods reduce cumulative errors and mismatches in multi-view scenarios.•Dynamic adjustment of template parameters, based on intermediate results, greatly enhances matching accuracy.•Improved sub-pixel registration accuracy increases efficiency and convergence in multi-view point cloud registration.
Multi-view matching is an indispensable method for digital image correlation 3D reconstruction. However, as the camera viewing angle changes, traditional DIC methods can lead to the accumulation of matching errors and an increase in mismatches, thereby affecting the 3D reconstruction quality. This paper proposes a digital image correlation method for multi-view RGB-D images based on variable template matching. Initial positioning is achieved by dynamically adjusting the size and sampling direction of the variable template. Sub-pixel registration is then performed using the inverse compositional algorithm, followed by multi-view point cloud fusion using the Iterative Closest Point (ICP) registration method. Experimental results show that the iteration speed of point cloud registration is increased by 30%, and the cumulative error is reduced by 62.5%. These improvements demonstrate that the proposed method is suitable for multi-viewpoint matching scenarios, achieving excellent results in multi-view feature point matching and multi-view point cloud fusion, significantly enhancing 3D reconstruction accuracy and efficiency. |
| ArticleNumber | 115617 |
| Author | Hu, Huiran Song, Aiguo |
| Author_xml | – sequence: 1 givenname: Huiran orcidid: 0000-0001-7511-8677 surname: Hu fullname: Hu, Huiran email: 18605592115@163.com organization: School of Instrument Science & Engineering, Southeast University, Nanjing, Jiangsu 210096, China – sequence: 2 givenname: Aiguo orcidid: 0000-0002-1982-6780 surname: Song fullname: Song, Aiguo email: a.g.song@seu.edu.cn organization: State Key Laboratory of Digital Medicine Engineering and the Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science & Engineering, Southeast University, Nanjing, Jiangsu 210096, China |
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