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Cross-source point cloud registration network with low overlap based on multi-scale features and attention mechanisms.

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Title: Cross-source point cloud registration network with low overlap based on multi-scale features and attention mechanisms.
Authors: ZHONG, Xingjian1, WANG, Peng1 wang_peng@tju.edu.cn, LI, Yue1, LI, Lin1, FU, Luhua1, SUN, Changku1
Source: Journal of Measurement Science & Instrumentation. Jun2026, Vol. 17 Issue 2, p183-194. 12p.
Subjects: Point cloud, Machine learning, Computer vision, Three-dimensional modeling
Abstract: Machine vision-based detection methods have been widely applied in the detection of aircraft skin damage. During drone inspection processes, a key step is to spatially locate high-resolution detailed images of aircraft skin from multiple angles onto a three-dimensional point cloud model of the aircraft. This relies on the rigid registration of image center position coordinate point cloud with the aircraft 3D point cloud. To address the issues of low accuracy and poor robustness encountered by existing registration algorithms when dealing with heterogeneous point clouds with significant differences in density and low overlap, this paper presents a novel cross-source point cloud registration network. The network integrates multi-scale information from the point cloud and employs an attention mechanism to identify representative overlapping points. First, the network achieves initial correspondences using the multi-scale geometric features and positional information of the point cloud. Then, an overlapping feature guidance module predicts the overlapping score of the point cloud. By utilizing information interaction through the attention mechanism, the network combines point overlapping scores with fused features to filter out representative overlapping points, achieving precise correspondences in the point cloud. The network employs weighted singular value decomposition (SVD) to estimate two sets of transformation matrices, yielding the relative pose parameters of the point cloud. Experiments were conducted in an unsupervised manner. The experimental results on the ModelNet40 dataset and the aero object dataset aircraft measurement data showed that, compared to other existing traditional and learning-based methods, this approach demonstrated excellent performance in terms of registration accuracy and robustness. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Cross-source point cloud registration network with low overlap based on multi-scale features and attention mechanisms.
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  Data: <searchLink fieldCode="AR" term="%22ZHONG%2C+Xingjian%22">ZHONG, Xingjian</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22WANG%2C+Peng%22">WANG, Peng</searchLink><relatesTo>1</relatesTo><i> wang_peng@tju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22LI%2C+Yue%22">LI, Yue</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22LI%2C+Lin%22">LI, Lin</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22FU%2C+Luhua%22">FU, Luhua</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22SUN%2C+Changku%22">SUN, Changku</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Measurement+Science+%26+Instrumentation%22">Journal of Measurement Science & Instrumentation</searchLink>. Jun2026, Vol. 17 Issue 2, p183-194. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Point+cloud%22">Point cloud</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Three-dimensional+modeling%22">Three-dimensional modeling</searchLink>
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  Data: Machine vision-based detection methods have been widely applied in the detection of aircraft skin damage. During drone inspection processes, a key step is to spatially locate high-resolution detailed images of aircraft skin from multiple angles onto a three-dimensional point cloud model of the aircraft. This relies on the rigid registration of image center position coordinate point cloud with the aircraft 3D point cloud. To address the issues of low accuracy and poor robustness encountered by existing registration algorithms when dealing with heterogeneous point clouds with significant differences in density and low overlap, this paper presents a novel cross-source point cloud registration network. The network integrates multi-scale information from the point cloud and employs an attention mechanism to identify representative overlapping points. First, the network achieves initial correspondences using the multi-scale geometric features and positional information of the point cloud. Then, an overlapping feature guidance module predicts the overlapping score of the point cloud. By utilizing information interaction through the attention mechanism, the network combines point overlapping scores with fused features to filter out representative overlapping points, achieving precise correspondences in the point cloud. The network employs weighted singular value decomposition (SVD) to estimate two sets of transformation matrices, yielding the relative pose parameters of the point cloud. Experiments were conducted in an unsupervised manner. The experimental results on the ModelNet40 dataset and the aero object dataset aircraft measurement data showed that, compared to other existing traditional and learning-based methods, this approach demonstrated excellent performance in terms of registration accuracy and robustness. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.62756/jmsi.1674-8042.2026016
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      – Code: eng
        Text: English
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        PageCount: 12
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        Type: general
      – SubjectFull: Machine learning
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      – SubjectFull: Computer vision
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      – SubjectFull: Three-dimensional modeling
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              M: 06
              Text: Jun2026
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              Y: 2026
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