{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T13:23:25Z","timestamp":1783517005419,"version":"3.55.0"},"reference-count":53,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42074041"],"award-info":[{"award-number":["42074041"]}]},{"name":"National Natural Science Foundation of China","award":["41904171"],"award-info":[{"award-number":["41904171"]}]},{"name":"National Natural Science Foundation of China","award":["KF-2021-06-102"],"award-info":[{"award-number":["KF-2021-06-102"]}]},{"name":"Open Fund of Key Laboratory of Urban Resources Monitoring and Simulation, Ministry of Natural Resources","award":["42074041"],"award-info":[{"award-number":["42074041"]}]},{"name":"Open Fund of Key Laboratory of Urban Resources Monitoring and Simulation, Ministry of Natural Resources","award":["41904171"],"award-info":[{"award-number":["41904171"]}]},{"name":"Open Fund of Key Laboratory of Urban Resources Monitoring and Simulation, Ministry of Natural Resources","award":["KF-2021-06-102"],"award-info":[{"award-number":["KF-2021-06-102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Point cloud registration (PCR) is a vital problem in remote sensing and computer vision, which has various important applications, such as 3D reconstruction, object recognition, and simultaneous localization and mapping (SLAM). Although scholars have investigated a variety of methods for PCR, the applications have been limited by low accuracy, high memory footprint, and slow speed, especially for dealing with a large number of point cloud data. To solve these problems, a novel local descriptor is proposed for efficient PCR. We formed a comprehensive description of local geometries with their statistical properties on a normal angle, dot product of query point normal and vector from the point to its neighborhood point, the distance between the query point and its neighborhood point, and curvature variation. Sub-features in descriptors were low-dimensional and computationally efficient. Moreover, we applied the optimized sample consensus (OSAC) algorithm to iteratively estimate the optimum transformation from point correspondences. OSAC is robust and practical for matching highly self-similar features. Experiments and comparisons with the commonly used descriptor were conducted on several synthetic datasets and our real scanned bridge data. The result of the simulation experiments showed that the rotation angle error was below 0.025\u00b0 and the translation error was below 0.0035 m. The real dataset was terrestrial laser scanning (TLS) data of Sujiaba Bridge in Chongqing, China. The results showed the proposed descriptor successfully registered the practical TLS data with the smallest errors. The experiments demonstrate that the proposed method is fast with high alignment accuracy and achieves a better performance than previous commonly used methods.<\/jats:p>","DOI":"10.3390\/rs14174346","type":"journal-article","created":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T00:19:01Z","timestamp":1662077941000},"page":"4346","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Establishment and Extension of a Fast Descriptor for Point Cloud Registration"],"prefix":"10.3390","volume":"14","author":[{"given":"Lidu","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongfu","family":"Xiang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6165-2158","authenticated-orcid":false,"given":"Maolin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China"},{"name":"Key Laboratory of Urban Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518308, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9657-3871","authenticated-orcid":false,"given":"Xiaping","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Geomatics, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yin","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0357-5312","authenticated-orcid":false,"given":"Shuangcheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuan","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaixin","family":"Hu","sequence":"additional","affiliation":[{"name":"Chongqing Smart City and Sustainble Development Academy, Chongqing 401135, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,1]]},"reference":[{"key":"ref_1","unstructured":"Remondino, F. (2003, January 24\u201328). From point cloud to surface: The modeling and visualization problem. Proceedings of the ISPRS WG V\/6 Workshop \u201cVisualization and Animation of Reality-Based 3D Models\u201d, Tarasp-Vulpera, Switzerland. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sahebdivani, S., Arefi, H., and Maboudi, M. (2020). Rail track detection and projection-based 3D modeling from UAV point cloud. Sensors, 20.","DOI":"10.3390\/s20185220"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s10015-020-00617-3","article-title":"Navigation of a mobile robot in a dynamic environment using a point cloud map","volume":"26","author":"Wang","year":"2021","journal-title":"Artif. Life Robot."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2955","DOI":"10.1007\/s12555-019-0313-0","article-title":"3D localization of a mobile robot by using Monte Carlo algorithm and 2D features of 3D point cloud","volume":"18","author":"Lee","year":"2020","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_5","first-page":"1729881417732757","article-title":"Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation","volume":"14","author":"Caballero","year":"2017","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_6","first-page":"43","article-title":"3D object recognition based on local and global features using point cloud library","volume":"7","author":"Alhamzi","year":"2015","journal-title":"Int. J. Adv. Comput. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"153","DOI":"10.24003\/emitter.v10i1.704","article-title":"Density-based Clustering for 3D Stacked Pipe Object Recognition using Directly-given Point Cloud Data on Convolutional Neural Network","volume":"10","author":"Pratama","year":"2022","journal-title":"EMITTER Int. J. Eng. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bai, X., Hu, Z., Zhu, X., Huang, Q., Chen, Y., Fu, H., and Tai, C. (2022, January 21\u201324). Transfusion: Robust lidar-camera fusion for 3D object detection with transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00116"},{"key":"ref_9","first-page":"81","article-title":"Novel algorithms for 3D surface point cloud boundary detection and edge reconstruction","volume":"6","author":"Mineo","year":"2019","journal-title":"J. Comput. Des. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2705","DOI":"10.1109\/TCSVT.2021.3095233","article-title":"Quadratic terms based point-to-surface 3D representation for deep learning of point cloud","volume":"32","author":"Sun","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9916859","DOI":"10.1155\/2021\/9916859","article-title":"Accurate virtual trial assembly method of prefabricated steel components using terrestrial laser scanning","volume":"2021","author":"Zhou","year":"2021","journal-title":"Adv. Civ. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhao, L., Ma, X., Xiang, Z., Zhang, S., Hu, C., Zhou, Y., and Chen, G. (2022). Landslide Deformation Extraction from Terrestrial Laser Scanning Data with Weighted Least Squares Regularization Iteration Solution. Remote Sens., 14.","DOI":"10.3390\/rs14122897"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/34.982886","article-title":"ICP registration using invariant features","volume":"24","author":"Sharp","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1109\/LRA.2021.3137503","article-title":"Stein ICP for Uncertainty Estimation in Point Cloud Matching","volume":"7","author":"Maken","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cheng, L., Chen, S., Liu, X., Xu, H., Wu, Y., Li, M., and Chen, Y. (2018). Registration of laser scanning point clouds: A review. Sensors, 18.","DOI":"10.3390\/s18051641"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2965","DOI":"10.1109\/TCSVT.2017.2730232","article-title":"A coarse-to-fine algorithm for matching and registration in 3D cross-source point clouds","volume":"28","author":"Huang","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_17","first-page":"23872","article-title":"Cofinet: Reliable coarse-to-fine correspondences for robust pointcloud registration","volume":"34","author":"Yu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1016\/j.imavis.2009.09.006","article-title":"A high-accuracy method for fine registration of overlapping point clouds","volume":"28","author":"Xie","year":"2010","journal-title":"Image Vis. Comput."},{"key":"ref_19","first-page":"2","article-title":"Feature-based registration of terrestrial lidar point clouds","volume":"37","author":"Jaw","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103992","DOI":"10.1016\/j.autcon.2021.103992","article-title":"Automated semantic segmentation of bridge point cloud based on local descriptor and machine learning","volume":"133","author":"Xia","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/34.993558","article-title":"Shape matching and object recognition using shape contexts","volume":"24","author":"Belongie","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","unstructured":"Makadia, A., Patterson, A., and Daniilidis, K. (2006, January 17\u201322). Fully automatic registration of 3D point clouds. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), New York, NY, USA."},{"key":"ref_23","unstructured":"Dold, C. (2005, January 12\u201314). Extended Gaussian images for the registration of terrestrial scan data. Proceedings of the ISPRS Workshop Laser Scanning, Enschede, The Netherlands."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Poiesi, F., and Boscaini, D. (2022). Generalisable and distinctive 3D local deep descriptors for point cloud registration. arXiv.","DOI":"10.1109\/TPAMI.2022.3175371"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bai, X., Luo, Z., Zhou, L., Fu, H., Quan, L., and Tai, C. (2020, January 13\u201319). D3feat: Joint learning of dense detection and description of 3D local features. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00639"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Petricek, T., and Svoboda, T. (2017). Point cloud registration from local feature correspondences\u2014Evaluation on challenging datasets. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0187943"},{"key":"ref_27","unstructured":"Huang, X., Mei, G., Zhang, J., and Abbas, R. (2021). A comprehensive survey on point cloud registration. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.cviu.2017.09.004","article-title":"A performance evaluation of point pair features","volume":"166","author":"Kiforenko","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/34.765655","article-title":"Using spin images for efficient object recognition in cluttered 3D scenes","volume":"21","author":"Johnson","year":"1999","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","unstructured":"Buch, A.G., Kraft, D., and Robotics, S. (2018, January 3\u20136). Local Point Pair Feature Histogram for Accurate 3D Matching. Proceedings of the BMVC, Newcastle, UK."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Tombari, F., Salti, S., and Stefano, L.D. (2010, January 5\u201311). Unique signatures of histograms for local surface description. Proceedings of the European Conference on Computer Vision, Heraklion, Greece.","DOI":"10.1007\/978-3-642-15558-1_26"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.cviu.2014.04.011","article-title":"SHOT: Unique signatures of histograms for surface and texture description","volume":"125","author":"Salti","year":"2014","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s11263-013-0627-y","article-title":"Rotational projection statistics for 3D local surface description and object recognition","volume":"105","author":"Guo","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.patcog.2016.11.019","article-title":"TOLDI: An effective and robust approach for 3D local shape description","volume":"65","author":"Yang","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bai, X., Luo, Z., Zhou, L., Chen, H., Li, L., Hu, Z., Fu, H., and Tai, C. (2021, January 20\u201325). Pointdsc: Robust point cloud registration using deep spatial consistency. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01560"},{"key":"ref_37","first-page":"33","article-title":"Recognising structure in laser scanner point clouds","volume":"46","author":"Vosselman","year":"2004","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_38","unstructured":"Pauly, M., Gross, M., and Kobbelt, L.P. (November, January 27). Efficient simplification of point-sampled surfaces. Proceedings of the IEEE Visualization 2002, VIS 2002, Boston, MA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.isprsjprs.2017.06.012","article-title":"A novel binary shape context for 3D local surface description","volume":"130","author":"Dong","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Blodow, N., Marton, Z.C., and Beetz, M. (2008, January 22\u201326). Aligning point cloud views using persistent feature histograms. Proceedings of the 2008 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Nice, France.","DOI":"10.1109\/IROS.2008.4650967"},{"key":"ref_41","unstructured":"Deakin, R.E. (2006). A Note on the Bursa-Wolf and Molodensky-Badekas Transformations, School of Mathematical and Geospatial Sciences, RMIT University."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.ins.2016.01.095","article-title":"A fast and robust local descriptor for 3D point cloud registration","volume":"346","author":"Yang","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_44","unstructured":"(2022, June 29). The Standford 3D Scanning Repository. Available online: http:\/\/www-graphics.stanford.edu\/data\/3Dscanrep\/."},{"key":"ref_45","unstructured":"(2022, August 19). Available online: http:\/\/vision.deis.unibo.it\/research\/80-shot."},{"key":"ref_46","unstructured":"(2022, June 29). Leica ScanStation P50\u2014Long Range 3D Terrestrial Laser Scanner. Available online: https:\/\/leica-geosystems.com\/products\/laser-scanners\/scanners\/leica-scanstation-p50."},{"key":"ref_47","first-page":"109","article-title":"Point cloud non local denoising using local surface descriptor similarity","volume":"38","author":"Deschaud","year":"2010","journal-title":"IAPRS"},{"key":"ref_48","first-page":"5701117","article-title":"A novel rock-mass point cloud registration method based on feature line extraction and feature point matching","volume":"60","author":"Liu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1109\/LRA.2017.2667721","article-title":"3DHoPD: A fast low-dimensional 3-D descriptor","volume":"2","author":"Prakhya","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_50","first-page":"3614","article-title":"Fast descriptors and correspondence propagation for robust global point cloud registration","volume":"26","author":"Lei","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Dong, T., Zhao, Y., Zhang, Q., Xue, B., Li, J., and Li, W. (2022). Multi-scale point cloud registration based on topological structure. Concurr. Comput. Pract. Exp., e6873.","DOI":"10.1002\/cpe.6873"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Lu, J., Wang, Z., Hua, B., and Chen, K. (2020). Automatic point cloud registration algorithm based on the feature histogram of local surface. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0238802"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"20423","DOI":"10.1364\/OE.425622","article-title":"ICP registration with DCA descriptor for 3D point clouds","volume":"29","author":"He","year":"2021","journal-title":"Opt. 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