{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T12:24:28Z","timestamp":1764937468592,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T00:00:00Z","timestamp":1664668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Opening Project of Cooperative Innovation Center for Nuclear Fuel Cycle Technology and Equipment, University of South China","award":["2019KFZ04","2021GK5049"],"award-info":[{"award-number":["2019KFZ04","2021GK5049"]}]},{"name":"Program of Science and Technology Commissioners of Hunan Province","award":["2019KFZ04","2021GK5049"],"award-info":[{"award-number":["2019KFZ04","2021GK5049"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the increase in the amount of 3D point cloud data and the wide application of point cloud registration in various fields, the question of whether it is possible to quickly extract the key points of registration and perform accurate coarse registration has become a question to be urgently answered. In this paper, we proposed a novel semantic segmentation algorithm that enables the extracted feature point cloud to have a clustering effect for fast registration. First of all, an adaptive technique was proposed to determine the domain radius of a local point. Secondly, the feature intensity of the point is scored through the regional fluctuation coefficient and stationary coefficient calculated by the normal vector, and the high feature region to be registered is preliminarily determined. In the end, FPFH is used to describe the geometric features of the extracted semantic feature point cloud, so as to realize the coarse registration from the local point cloud to the overall point cloud. The results show that the point cloud can be roughly segmented based on the uniqueness of semantic features. The use of a semantic feature point cloud can make the point cloud have a very fast response speed based on the accuracy of coarse registration, almost equal to that of using the original point cloud, which is conducive to the rapid determination of the initial attitude.<\/jats:p>","DOI":"10.3390\/s22197479","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:07:28Z","timestamp":1665371248000},"page":"7479","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Fast Registration of Point Cloud Based on Custom Semantic Extraction"],"prefix":"10.3390","volume":"22","author":[{"given":"Jianing","family":"Wu","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, University of South China, Hengyang 421001, China"}]},{"given":"Zhang","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, University of South China, Hengyang 421001, China"}]},{"given":"Fan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Wealth Management, Ningbo University of Finance & Economics, Ningbo 315000, China"}]},{"given":"Tianlin","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, University of South China, Hengyang 421001, China"}]},{"given":"Zhi","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Glasgow, Glasgow G12 8RZ, UK"}]},{"given":"Fengwei","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, University of South China, Hengyang 421001, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,2]]},"reference":[{"key":"ref_1","first-page":"315","article-title":"3Dlocalization and Mapping of Outdoor Mobile Robots Using a LIDAR","volume":"43","author":"Han","year":"2015","journal-title":"J. Huazhong Univ. Ence Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"289","DOI":"10.5194\/isprs-archives-XLII-1-W1-289-2017","article-title":"An Efficient Method to Create Digital Terrain Models from Point Clouds Collected by Mobile Lidar Systems","volume":"XLII-1\/W1","author":"Antunes","year":"2017","journal-title":"ISPRS\u2014Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_3","unstructured":"Va, G., Ghgb, B., Sb, G., and Rb, T. (2004, January 3\u20136). Recognising Structure in Laser Scanner Point Clouds. Proceedings of the ISPRS Working Group VIII\/2: Laser Scanning for Forest and Landscape Assessment, Freiburg, Germany."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.isprsjprs.2009.04.001","article-title":"Knowledge Based Reconstruction of Building Models from Terrestrial Laser Scanning Data","volume":"64","author":"Pu","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3229","DOI":"10.1007\/s11227-019-02747-3","article-title":"Real-Time 3D Reconstruction Method Using Massive Multi-Sensor Data Analysis and Fusion","volume":"75","author":"Cho","year":"2019","journal-title":"J. Supercomput."},{"key":"ref_6","unstructured":"Lovi, D., Birkbeck, N., Cobza, D., and Jgersand, M. (2011). Incremental Free-Space Carving for Real-Time 3D Reconstruction. [Master\u2019s Thesis, University of Alberta]."},{"key":"ref_7","unstructured":"Yue, M.A., Wei, Z.C., and Wang, Y. (2014, January 20\u201321). Point Cloud Feature Extraction Based Integrated Positioning Method for Unmanned Vehicle. Proceedings of the 2014 International Conference on Applied Mechanics and Mechanical Automation (AMMA 2014), Macau, China."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.compag.2017.12.034","article-title":"Mapping Forests Using an Unmanned Ground Vehicle with 3D LiDAR and Graph-SLAM","volume":"145","author":"Pierzcha","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","unstructured":"Herbert, H.E., and Ray, M.D. (2021). Self-Contained Mapping and Positioning System Utilizing Point Cloud Data. (Application No. CA2347569C), Canada Patent."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1016\/j.imavis.2006.05.012","article-title":"A Review of Recent Range Image Registration Methods with Accuracy Evaluation","volume":"25","author":"Salvi","year":"2007","journal-title":"Image Vis. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1109\/TPAMI.2014.2316828","article-title":"3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey","volume":"36","author":"Guo","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2014.09.010","article-title":"Evaluation of Feature-Based 3-d Registration of Probabilistic Volumetric Scenes","volume":"98","author":"Restrepo","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","unstructured":"Besl, P.J., and Mckay, N.D. (1991, January 14\u201315). Method for Registration of 3-D Shapes. Proceedings of the Sensor Fusion IV: Control Paradigms and Data Structures, ROBOTICS\u201891, Boston, MA, USA."},{"key":"ref_14","unstructured":"Rusinkiewicz, S., and Levoy, M. (June, January 28). Efficient Variants of the ICP Algorithm. Proceedings of the Third International Conference on 3-D Digital Imaging and Modeling, Quebec City, QC, Canada."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Agamennoni, G., Fontana, S., Siegwart, R.Y., and Sorrenti, D.G. (2016, January 9\u201314). Point Clouds Registration with Probabilistic Data Association. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots & Systems, Daejeon, Korea.","DOI":"10.1109\/IROS.2016.7759602"},{"key":"ref_16","first-page":"145","article-title":"Object Modeling by Registration of Multiple Range Images","volume":"10","author":"Yang","year":"2002","journal-title":"Image Vis. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.ijleo.2017.01.041","article-title":"An Improved Method for Registration of Point Cloud","volume":"140","author":"Ji","year":"2017","journal-title":"Opt.\u2014Int. J. Light Electron Opt."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, S., Gao, D., Wang, P., Guo, X., Xu, J., and Liu, D.-X. (2018). A Depth-Based Weighted Point Cloud Registration for Indoor Scene. Sensors, 18.","DOI":"10.3390\/s18113608"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kamencay, P., Sinko, M., Hudec, R., Benco, M., and Radil, R. (2019, January 1\u20133). Improved Feature Point Algorithm for 3D Point Cloud Registration. Proceedings of the 2019 42nd International Conference on Telecommunications and Signal Processing (TSP), Budapest, Hungary.","DOI":"10.1109\/TSP.2019.8769057"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yang, Y., Li, H., Yang, J., and Zhong, D. (2019, January 6\u20139). Structured Down-Sampling and Registration Method for 3D Point Cloud of Indoor Scene. Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy.","DOI":"10.1109\/SMC.2019.8914420"},{"key":"ref_21","unstructured":"B\u00f6hm, J., and Becker, S. (2007, January 9\u201312). Automatic Marker-Free Registration of Terrestrial Laser Scans Using Reflectance Features. Proceedings of the 8th Conference on Optical 3D Measurement Techniques, Zurich, Switzerland."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.isprsjprs.2007.05.005","article-title":"Keypoint Based Autonomous Registration of Terrestrial Laser Point-Clouds","volume":"63","author":"Barnea","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","unstructured":"Rusu, R.B., Marton, Z.C., Blodow, N., and Beetz, M. (2008). Persistent Point Feature Histograms for 3D Point Clouds. Intelligent Autonomous Systems 10, IOS Press."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1007\/s00371-011-0610-y","article-title":"Harris 3D: A Robust Extension of the Harris Operator for Interest Point Detection on 3D Meshes","volume":"27","author":"Sipiran","year":"2011","journal-title":"Vis. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"115202","DOI":"10.1088\/1361-6501\/aadff6","article-title":"Feature Extraction from Point Clouds for Rigid Aircraft Part Inspection Using an Improved Harris Algorithm","volume":"29","author":"Qi","year":"2018","journal-title":"Meas. Sci. Technol."},{"key":"ref_27","unstructured":"Ye, Q., Liu, H., and Lin, Y. (2021, January 23\u201326). Study of RGB-D Point Cloud Registration Method Guided by Color Information. Proceedings of the International Conference on Information Optics and Photonics, Xi\u2019an, China."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1007\/s00006-018-0864-9","article-title":"A Curvature-Based Descriptor for Point Cloud Alignment Using Conformal Geometric Algebra","volume":"28","author":"Kleppe","year":"2018","journal-title":"Adv. Appl. Clifford Algebras"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s11704-016-6191-1","article-title":"A Fast Registration Algorithm of Rock Point Cloud Based on Spherical Projection and Feature Extraction","volume":"13","author":"Xian","year":"2019","journal-title":"Front. Comput. Sci."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"Ye, S., Chen, D., Han, S., Wan, Z., and Liao, J. (2021). Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud. arXiv.","DOI":"10.1109\/TVCG.2021.3058311"},{"key":"ref_32","unstructured":"Zhou, W., Yang, Q., Jiang, Q., Zhai, G., and Lin, W. (2022). Blind Quality Assessment of 3D Dense Point Clouds with Structure Guided Resampling. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7479\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:45:35Z","timestamp":1760143535000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7479"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,2]]},"references-count":32,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22197479"],"URL":"https:\/\/doi.org\/10.3390\/s22197479","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,10,2]]}}}