3D Point Cloud Fusion Method Based on EMD Auto-Evolution and Local Parametric Network
Although the development of high-resolution remote sensing satellite technology has made it possible to reconstruct the 3D structure of object-level features using satellite imagery, the results from a single reconstruction are often insufficient to comprehensively describe the 3D structure of the t...
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/22/4219 |
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| _version_ | 1846152556022071296 |
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| author | Wen Chen Hao Chen Shuting Yang |
| author_facet | Wen Chen Hao Chen Shuting Yang |
| author_sort | Wen Chen |
| collection | DOAJ |
| description | Although the development of high-resolution remote sensing satellite technology has made it possible to reconstruct the 3D structure of object-level features using satellite imagery, the results from a single reconstruction are often insufficient to comprehensively describe the 3D structure of the target. Therefore, developing an effective 3D point cloud fusion method can fully utilize information from multiple observations to improve the accuracy of 3D reconstruction. To this end, this paper addresses the problems of shape distortion and sparse point cloud density in existing 3D point cloud fusion methods by proposing a 3D point cloud fusion method based on Earth mover’s distance (EMD) auto-evolution and local parameterization network. Our method is divided into two stages. In the first stage, EMD is introduced as a key metric for evaluating the fusion results, and a point cloud fusion method based on EMD auto-evolution is constructed. The method uses an alternating iterative technique to sequentially update the variables and produce an initial fusion result. The second stage focuses on point cloud optimization by constructing a local parameterization network for the point cloud, mapping the upsampled point cloud in the 2D parameter domain back to the 3D space to complete the optimization. Through these two steps, the method achieves the fusion of two sets of non-uniform point cloud data obtained from satellite stereo images into a single, denser 3D point cloud that more closely resembles the true target shape. Experimental results demonstrate that our fusion method outperforms other classical comparison algorithms for targets such as buildings, planes, and ships, and achieves a fused RMSE of approximately 2 m and an EMD accuracy better than 0.5. |
| format | Article |
| id | doaj-art-3baf1e2f1e8d4a0e8b4e07f186d419d8 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-3baf1e2f1e8d4a0e8b4e07f186d419d82024-11-26T18:20:02ZengMDPI AGRemote Sensing2072-42922024-11-011622421910.3390/rs162242193D Point Cloud Fusion Method Based on EMD Auto-Evolution and Local Parametric NetworkWen Chen0Hao Chen1Shuting Yang2School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150006, ChinaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150006, ChinaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150006, ChinaAlthough the development of high-resolution remote sensing satellite technology has made it possible to reconstruct the 3D structure of object-level features using satellite imagery, the results from a single reconstruction are often insufficient to comprehensively describe the 3D structure of the target. Therefore, developing an effective 3D point cloud fusion method can fully utilize information from multiple observations to improve the accuracy of 3D reconstruction. To this end, this paper addresses the problems of shape distortion and sparse point cloud density in existing 3D point cloud fusion methods by proposing a 3D point cloud fusion method based on Earth mover’s distance (EMD) auto-evolution and local parameterization network. Our method is divided into two stages. In the first stage, EMD is introduced as a key metric for evaluating the fusion results, and a point cloud fusion method based on EMD auto-evolution is constructed. The method uses an alternating iterative technique to sequentially update the variables and produce an initial fusion result. The second stage focuses on point cloud optimization by constructing a local parameterization network for the point cloud, mapping the upsampled point cloud in the 2D parameter domain back to the 3D space to complete the optimization. Through these two steps, the method achieves the fusion of two sets of non-uniform point cloud data obtained from satellite stereo images into a single, denser 3D point cloud that more closely resembles the true target shape. Experimental results demonstrate that our fusion method outperforms other classical comparison algorithms for targets such as buildings, planes, and ships, and achieves a fused RMSE of approximately 2 m and an EMD accuracy better than 0.5.https://www.mdpi.com/2072-4292/16/22/4219satellite stereo images3D point cloud fusionEMD auto-evolutionlocal parameterization network |
| spellingShingle | Wen Chen Hao Chen Shuting Yang 3D Point Cloud Fusion Method Based on EMD Auto-Evolution and Local Parametric Network Remote Sensing satellite stereo images 3D point cloud fusion EMD auto-evolution local parameterization network |
| title | 3D Point Cloud Fusion Method Based on EMD Auto-Evolution and Local Parametric Network |
| title_full | 3D Point Cloud Fusion Method Based on EMD Auto-Evolution and Local Parametric Network |
| title_fullStr | 3D Point Cloud Fusion Method Based on EMD Auto-Evolution and Local Parametric Network |
| title_full_unstemmed | 3D Point Cloud Fusion Method Based on EMD Auto-Evolution and Local Parametric Network |
| title_short | 3D Point Cloud Fusion Method Based on EMD Auto-Evolution and Local Parametric Network |
| title_sort | 3d point cloud fusion method based on emd auto evolution and local parametric network |
| topic | satellite stereo images 3D point cloud fusion EMD auto-evolution local parameterization network |
| url | https://www.mdpi.com/2072-4292/16/22/4219 |
| work_keys_str_mv | AT wenchen 3dpointcloudfusionmethodbasedonemdautoevolutionandlocalparametricnetwork AT haochen 3dpointcloudfusionmethodbasedonemdautoevolutionandlocalparametricnetwork AT shutingyang 3dpointcloudfusionmethodbasedonemdautoevolutionandlocalparametricnetwork |