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Of the available algorithms, the Iterative Closest Point (ICP) algorithm has been used as the classic algorithm for solving point cloud registration. However, with the point cloud data being under the influence of noise, outliers, overlapping values, and other issues, the performance of the ICP algorithm will be affected to varying degrees. This paper proposes a global structure and adaptive weight aware ICP algorithm (GSAW-ICP) for image registration. Specifically, we first proposed a global structure mathematical model based on the reconstruction of local surfaces using both the rotation of normal vectors and the change in curvature, so as to better describe the deformation of the object. The model was optimized for the convergence strategy, so that it had a wider convergence domain and a better convergence effect than either of the original point-to-point or point-to-point constrained models. Secondly, for outliers and overlapping values, the GSAW-ICP algorithm was able to assign appropriate weights, so as to optimize both the noise and outlier interference of the overall system. Our proposed algorithm was extensively tested on noisy, anomalous, and real datasets, and the proposed method was proven to have a better performance than other state-of-the-art algorithms.<\/jats:p>","DOI":"10.3390\/rs15123185","type":"journal-article","created":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T01:59:30Z","timestamp":1687226370000},"page":"3185","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Global Structure and Adaptive Weight Aware ICP Algorithm for Image Registration"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0875-1549","authenticated-orcid":false,"given":"Lin","family":"Cao","sequence":"first","affiliation":[{"name":"The Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing 100101, China"},{"name":"The Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengbin","family":"Zhuang","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing 100101, China"},{"name":"The Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu","family":"Tian","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing 100101, China"},{"name":"The Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongmin","family":"Zhao","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing 100101, China"},{"name":"The Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4549-744X","authenticated-orcid":false,"given":"Chong","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Cfiguromputer Science and Engineering, Northeastern University, Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanan","family":"Guo","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing 100101, China"},{"name":"The Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing TransMicrowave Technology Company, Beijing 100080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Giancola, S., Zarzar, J., and Ghanem, B. (2019, January 15\u201320). Leveraging Shape Completion for 3D Siamese Tracking. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00145"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3306346.3323037","article-title":"A symmetric objective function for ICP","volume":"38","author":"Rusinkiewicz","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"ref_3","unstructured":"Besl, P.J., and McKay, N.D. (1992, January 30). A method for registration of 3-D shapes. Proceedings of the Sensor Fusion IV: Control Paradigms and Data Structures, Boston, MA, USA."},{"key":"ref_4","unstructured":"Huang, X., Mei, G., Zhang, J., and Abbas, R. (2021). A comprehensive survey on point cloud registration. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s11263-006-5167-2","article-title":"Geometry and convergence analysis of algorithms for registration of 3D shapes","volume":"67","author":"Pottmann","year":"2006","journal-title":"Int. J. Comput. Vis."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0262-8856(92)90066-C","article-title":"Object modelling by registration of multiple range images","volume":"10","author":"Chen","year":"1992","journal-title":"Image Vis. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.cviu.2004.04.002","article-title":"Registration without ICP","volume":"95","author":"Pottmann","year":"2004","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.isprsjprs.2022.01.019","article-title":"Robust symmetric iterative closest point","volume":"185","author":"Li","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","first-page":"3450","article-title":"Fast and robust iterative closest point","volume":"44","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1111\/cgf.12178","article-title":"Sparse iterative closest point","volume":"32","author":"Bouaziz","year":"2013","journal-title":"Computer Graphics Forum"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.neucom.2022.08.047","article-title":"Adaptive weighted robust iterative closest point","volume":"508","author":"Guo","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.isprsjprs.2020.07.012","article-title":"GESAC: Robust graph enhanced sample consensus for point cloud registration","volume":"167","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, W., de Gusmao, P.P.B., Yang, B., Markham, A., and Trigoni, N. (June, January 30). Radarloc: Learning to relocalize in fmcw radar. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9560858"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"You, Y., Lou, Y., Li, C., Cheng, Z., Li, L., Ma, L., Lu, C., and Wang, W. (2020, January 13\u201319). Keypointnet: A large-scale 3d keypoint dataset aggregated from numerous human annotations. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01366"},{"key":"ref_15","unstructured":"Yuan, T.W., Lu, Y.N., Shi, Z.K., and Zhang, Z. (2016). Fuzzy Systems and Data Mining II, IOS Press."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, J., and Lee, G.H. (2019, January 20\u201323). Usip: Unsupervised stable interest point detection from 3d point clouds. Proceedings of the IEEE\/CVF international conference on computer vision, Cambridge, MA, USA.","DOI":"10.1109\/ICCV.2019.00045"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Blodow, N., and Beetz, M. (2009, January 12\u201317). Fast point feature histograms (FPFH) for 3D registration. Proceedings of the 2009 IEEE international conference on robotics and automation, Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152473"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zeng, A., Song, S., Nie\u00dfner, M., Fisher, M., Xiao, J., and Funkhouser, T. (2017, January 21\u201326). 3dmatch: Learning local geometric descriptors from rgb-d reconstructions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.29"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1049\/ell2.12199","article-title":"Absolute localisation in confined spaces using deep geometric features","volume":"57","author":"Brogaard","year":"2021","journal-title":"Electron. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xie, S., Gu, J., Guo, D., Qi, C.R., Guibas, L., and Litany, O. (2020, January 23\u201328). Pointcontrast: Unsupervised pre-training for 3d point cloud understanding. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK.","DOI":"10.1007\/978-3-030-58580-8_34"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ao, S., Hu, Q., Yang, B., Markham, A., and Guo, Y. (2021, January 20\u201325). Spinnet: Learning a general surface descriptor for 3d point cloud registration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01158"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhong, Y. (October, January 27). Intrinsic shape signatures: A shape descriptor for 3D object recognition. Proceedings of the 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, Kyoto, Japan.","DOI":"10.1109\/ICCVW.2009.5457637"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"C1","DOI":"10.1111\/ectj.12097","article-title":"Double\/debiased machine learning for treatment and structural parameters","volume":"21","author":"Chernozhukov","year":"2018","journal-title":"Econom. J."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Granger, S., and Pennec, X. (2002, January 28\u201331). Multi-scale EM-ICP: A fast and robust approach for surface registration. Proceedings of the Computer Vision\u2014ECCV 2002: 7th European Conference on Computer Vision, Copenhagen, Denmark.","DOI":"10.1007\/3-540-47979-1_28"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/TPWRS.2009.2030271","article-title":"Statistical representation of distribution system loads using Gaussian mixture model","volume":"25","author":"Singh","year":"2009","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9716","DOI":"10.1109\/TGRS.2020.3045456","article-title":"Point cloud registration based on one-point ransac and scale-annealing biweight estimation","volume":"59","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chum, O., Matas, J., and Kittler, J. (2003, January 10\u201312). Locally optimized RANSAC. Proceedings of the Pattern Recognition: 25th DAGM Symposium, Magdeburg, Germany.","DOI":"10.1007\/978-3-540-45243-0_31"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, Z., Chen, Z., Xie, S., and Zheng, W.-S. (2022, January 23\u201327). TransGrasp: A Multi-Scale Hierarchical Point Transformer for 7-DoF Grasp Detection. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9812001"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e1236","DOI":"10.1002\/widm.1236","article-title":"Anomaly detection by robust statistics","volume":"8","author":"Rousseeuw","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_31","first-page":"364","article-title":"An efficient and accurate camera calibration technique for 3D machine vision","volume":"1986","author":"Tsai","year":"1986","journal-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The kitti dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3185\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:56:33Z","timestamp":1760126193000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3185"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,19]]},"references-count":32,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15123185"],"URL":"https:\/\/doi.org\/10.3390\/rs15123185","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,19]]}}}