{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:27:55Z","timestamp":1760239675932,"version":"build-2065373602"},"reference-count":85,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,12,19]],"date-time":"2020-12-19T00:00:00Z","timestamp":1608336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation of China project","award":["41701529"],"award-info":[{"award-number":["41701529"]}]},{"name":"OpenFund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University","award":["18S02"],"award-info":[{"award-number":["18S02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Heritage documentation is implemented by digitally recording historical artifacts for the conservation and protection of these cultural heritage objects. As efficient spatial data acquisition tools, laser scanners have been widely used to collect highly accurate three-dimensional (3D) point clouds without damaging the original structure and the environment. To ensure the integrity and quality of the collected data, field inspection (i.e., on-spot checking the data quality) should be carried out to determine the need for additional measurements (i.e., extra laser scanning for areas with quality issues such as data missing and quality degradation). To facilitate inspection of all collected point clouds, especially checking the quality issues in overlaps between adjacent scans, all scans should be registered together. Thus, a point cloud registration method that is able to register scans fast and robustly is required. To fulfill the aim, this study proposes an efficient probabilistic registration for free-form cultural heritage objects by integrating the proposed principal direction descriptor and curve constraints. We developed a novel shape descriptor based on a local frame of principal directions. Within the frame, its density and distance feature images were generated to describe the shape of the local surface. We then embedded the descriptor into a probabilistic framework to reject ambiguous matches. Spatial curves were integrated as constraints to delimit the solution space. Finally, a multi-view registration was used to refine the position and orientation of each scan for the field inspection. Comprehensive experiments show that the proposed method was able to perform well in terms of rotation error, translation error, robustness, and runtime and outperformed some commonly used approaches.<\/jats:p>","DOI":"10.3390\/ijgi9120759","type":"journal-article","created":{"date-parts":[[2020,12,20]],"date-time":"2020-12-20T22:33:53Z","timestamp":1608503633000},"page":"759","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Efficient Probabilistic Registration Based on Shape Descriptor for Heritage Field Inspection"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3524-9629","authenticated-orcid":false,"given":"Yufu","family":"Zang","sequence":"first","affiliation":[{"name":"School of Remote Sensing &amp; Geomatics Engineering, Nanjing University of Information Science &amp; Technology, 219 Ningliu Road, Nanjing 210044, China"},{"name":"Department of Geoscience and Remote Sensing, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7180-7627","authenticated-orcid":false,"given":"Bijun","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3035-7727","authenticated-orcid":false,"given":"Xiongwu","family":"Xiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianfeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Jiangxi College of Applied Technology, Ganzhou 341000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fancong","family":"Meng","sequence":"additional","affiliation":[{"name":"Department of Geoscience and Remote Sensing, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,19]]},"reference":[{"key":"ref_1","first-page":"e00133","article-title":"Heritage documentation using laser scanner and photogrammetry. The case study of Qasr Al-Abidit, Jordan","volume":"16","author":"Alshawabkeh","year":"2020","journal-title":"Digit. Appl. Archaeol. Cult. Herit."},{"key":"ref_2","first-page":"549","article-title":"Multi-wavelengths 3D laser scanning for pigment and structural studies on the frescoed ceiling the triumph of divine providence","volume":"2","author":"Guarneri","year":"2019","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"71","DOI":"10.5194\/isprs-annals-IV-5-W1-71-2017","article-title":"Photogrammetric measurements of heritage objects","volume":"4","author":"Tumeliene","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kushwaha, S.K.P., Dayal, K.R., Raghavendra, S., Pande, H., Tiwari, P.S., Agrawal, S., and Srivastava, S.K. (2020). 3D Digital documentation of a cultural heritage site using terrestrial laser scanner\u2014A case study. Applications of Geomatics in Civil Engineering, Springer.","DOI":"10.1007\/978-981-13-7067-0_3"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"123","DOI":"10.5194\/isprs-annals-IV-4-W4-123-2017","article-title":"Laser Scanner Technology, Ground-Penetrating Radar and Augmented Reality for the Survey and Recovery of Artistic, Archaeological and Cultural Heritage","volume":"4","author":"Barrile","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Selbesoglu, M.O., Bakirman, T., and Gokbayrak, O. (2016). Deformation Measurement Using Terrestrial Laser Scanner For Cultural Heritage. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 89\u201393.","DOI":"10.5194\/isprs-archives-XLII-2-W1-89-2016"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ceccarelli, S., Guarneri, M., De Collibus, M.F., Francucci, M., Ciaffi, M., and Danielis, A. (2018). Laser Scanners for High-Quality 3D and IR Imaging in Cultural Heritage Monitoring and Documentation. J. Imaging, 4.","DOI":"10.3390\/jimaging4110130"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"32007","DOI":"10.1088\/1755-1315\/95\/3\/032007","article-title":"Application of Integrated Photogrammetric and Terrestrial Laser Scanning Data to Cultural Heritage Surveying","volume":"95","author":"Klapa","year":"2017","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jo, Y.H., and Hong, S. (2019). Three-Dimensional Digital Documentation of Cultural Heritage Site Based on the Convergence of Terrestrial Laser Scanning and Unmanned Aerial Vehicle Photogrammetry. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8020053"},{"key":"ref_10","unstructured":"R\u00fcther, H., Held, C., Bhurtha, R., Schr\u00f6der, R., and Wessels, S. (2011, January 30). Challenges in heritage documentation with terrestrial laser scanning. Proceedings of the 1st AfricaGEO Conference, Capetown, South Africa."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Markiewicz, J., and Zawieska, D. (2020). Analysis of the Selection Impact of 2D Detectors on the Accuracy of Image-Based TLS Data Registration of Objects of Cultural Heritage and Interiors of Public Utilities. Sensors, 20.","DOI":"10.3390\/s20113277"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"467","DOI":"10.5194\/isprs-archives-XLII-2-W9-467-2019","article-title":"The comparision of 2D and 3D detectors for TLS data registration\u2013preliminary results","volume":"XLII-2\/W9","author":"Markiewicz","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1109\/TPAMI.2009.155","article-title":"WLD: A Robust Local Image Descriptor","volume":"32","author":"Chen","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhu, J., and Fang, Y. (2020, January 1\u20135). Reference Grid-assisted Network for 3D Point Signature Learning from Point Clouds. Proceedings of the 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093270"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, X., Liu, J., and Zhao, H. (2016, January 3\u20134). Point cloud registration by discrete spin image and normal alignment radial feature. Proceedings of the 15th ACM SIGGRAPH Conference on Virtual-Reality Continuum and Its Applications in Industry, Zhuhai, China.","DOI":"10.1145\/3013971.3013994"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, F., Ye, Y., Hu, X., and Shan, J. (2016, January 4). Point cloud registration by combining shape and intensity contexts. Proceedings of the 2016 9th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), Cancun, Mexico.","DOI":"10.1109\/PRRS.2016.7867025"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1750006","DOI":"10.1142\/S0219467817500061","article-title":"Wide Angle Rigid Registration Using a Comparative Tensor Shape Factor","volume":"17","author":"Cejnog","year":"2017","journal-title":"Int. J. Image Graph."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Tombari, F., Salti, S., and Di Stefano, L. (2010). Unique signatures of histograms for local surface description. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-642-15558-1_26"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ragb, H.K., and Asari, V.K. (2016, January 18\u201320). Multi-feature fusion and PCA based approach for efficient human detection. Proceedings of the 2016 IEEE Applied Imagery Pattern Recognition Workshop, Washington, DC, USA.","DOI":"10.1109\/AIPR.2016.8010546"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1007\/s11263-015-0820-2","article-title":"A 3D Scene Registration Method via Covariance Descriptors and an Evolutionary Stable Strategy Game Theory Solver","volume":"115","author":"Cirujeda","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.isprsjprs.2014.05.012","article-title":"Automated registration of dense terrestrial laser-scanning point clouds using curves","volume":"95","author":"Yang","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.mechatronics.2015.10.014","article-title":"3D reconstruction and multiple point cloud registration using a low precision RGB-D sensor","volume":"35","author":"Takimoto","year":"2016","journal-title":"Mechatronics"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Huhle, B., Magnusson, M., Strasser, W., and Lilienthal, A.J. (2008, January 19\u201323). Registration of colored 3D point clouds with a Kernel-based extension to the normal distributions transform. Proceedings of the 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA.","DOI":"10.1109\/ROBOT.2008.4543829"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Della Corte, B., Bogoslavskyi, I., Stachniss, C., and Grisetti, G. (2018, January 21\u201325). A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8461049"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.robot.2016.10.016","article-title":"Multi-Channel Generalized-ICP: A robust framework for multi-channel scan registration","volume":"87","author":"Servos","year":"2017","journal-title":"Robot. Auton. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2942","DOI":"10.1109\/LRA.2018.2848308","article-title":"Integrating Deep Semantic Segmentation Into 3-D Point Cloud Registration","volume":"3","author":"Zaganidis","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Park, J., Zhou, Q.-Y., and Koltun, V. (2017, January 22\u201329). Colored Point Cloud Registration Revisited. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.25"},{"key":"ref_30","unstructured":"Akca, D. (2006). Registration of point clouds using range and intensity information. Recording, Modeling and Visualization of Cultural Heritage, Taylor & Francis."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Urschler, M., Bauer, J., Ditt, H., and Bischof, H. (2006). SIFT and shape context for feature-based nonlinear registration of thoracic CT images. International Workshop on Computer Vision Approaches to Medical Image Analysis, Springer.","DOI":"10.1007\/11889762_7"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s13319-018-0193-8","article-title":"3D Point Cloud Initial Registration Using Surface Curvature and SURF Matching","volume":"9","author":"Tong","year":"2018","journal-title":"3D Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.imavis.2016.09.002","article-title":"An automatic 3D point cloud registration method based on regional curvature maps","volume":"56","author":"Sun","year":"2016","journal-title":"Image Vis. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"187","DOI":"10.5194\/isprs-archives-XLI-B3-187-2016","article-title":"Detection of geometric keypoints and its application to point cloud coarse registration","volume":"41","author":"Bueno","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"10207","DOI":"10.1007\/s11042-016-3606-9","article-title":"A non-rigid 3D model retrieval method based on scale-invariant heat kernel signature features","volume":"76","author":"Li","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"106359","DOI":"10.1016\/j.optlastec.2020.106359","article-title":"Target recognition of ladar range images using modified local surface patches","volume":"130","author":"Zhai","year":"2020","journal-title":"Opt. Laser Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2015.01.005","article-title":"A novel cortical thickness estimation method based on volumetric Laplace\u2013Beltrami operator and heat kernel","volume":"22","author":"Wang","year":"2015","journal-title":"Med. Image Anal."},{"key":"ref_39","first-page":"609","article-title":"Automatic Feature Detection, Description and Matching from Mobile Laser Scanning Data and Aerial Imagery","volume":"XLI-B1","author":"Hussnain","year":"2016","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.neucom.2014.09.029","article-title":"An efficient registration algorithm based on spin image for LiDAR 3D point cloud models","volume":"151","author":"He","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.isprsjprs.2018.06.018","article-title":"Hierarchical registration of unordered TLS point clouds based on binary shape context descriptor","volume":"144","author":"Dong","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.autcon.2017.06.016","article-title":"Automatic point cloud coarse registration using geometric keypoint descriptors for indoor scenes","volume":"81","author":"Bueno","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_44","first-page":"295","article-title":"Evaluate 3D laser point clouds registration for cultural heritage documentation","volume":"21","author":"Shanoer","year":"2018","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_45","first-page":"W8","article-title":"Automatic 3D point cloud registration for cultural heritage documentation","volume":"Volume XXXVIII(3)","author":"Tournas","year":"2009","journal-title":"Proceedings of the Laser Scanning"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.culher.2018.07.013","article-title":"Automated markerless registration of point clouds from TLS and structured light scanner for heritage documentation","volume":"35","author":"Shao","year":"2019","journal-title":"J. Cult. Herit."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.isprsjprs.2007.05.012","article-title":"A method for automated registration of unorganised point clouds","volume":"63","author":"Bae","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"8962","DOI":"10.1109\/TIM.2020.2999137","article-title":"Line-Based 2-D\u20133-D Registration and Camera Localization in Structured Environments","volume":"69","author":"Yu","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2013\/243021","article-title":"A Morphological Model for Extracting Road Networks from High-Resolution Satellite Images","volume":"2013","author":"Awad","year":"2013","journal-title":"J. Eng."},{"key":"ref_50","first-page":"391","article-title":"Satellite imagery quality evaluation using image quality metrics for quantitative cadastral analysis","volume":"1","author":"Babawuro","year":"2011","journal-title":"Int. J. Comput. Appl. Eng. Sci."},{"key":"ref_51","first-page":"30","article-title":"Satellite imagery cadastral features extractions using image processing algorithms: A viable option for cadastral science","volume":"9","author":"Babawuro","year":"2012","journal-title":"Int. J. Comput. Sci. Issues"},{"key":"ref_52","first-page":"215","article-title":"Urban object extraction from digital surface model and digital aerial images","volume":"22","author":"Grigillo","year":"2012","journal-title":"Proc. ISPRS"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wu, J., Jie, S., Yao, W., and Stilla, U. (2011, January 11\u201313). Building boundary improvement for true orthophoto generation by fusing airborne LiDAR data. Proceedings of the Joint Urban Remote Sensing Event (JURSE), Munich, Germany.","DOI":"10.1109\/JURSE.2011.5764735"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"328","DOI":"10.20965\/ijat.2018.p0328","article-title":"Efficient Registration of Laser-Scanned Point Clouds of Bridges Using Linear Features","volume":"12","author":"Date","year":"2018","journal-title":"Int. J. Autom. Technol."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Cui, T., Ji, S., Shan, J., Gong, J., and Liu, K. (2017). Line-Based Registration of Panoramic Images and LiDAR Point Clouds for Mobile Mapping. Sensors, 17.","DOI":"10.20944\/preprints201612.0016.v1"},{"key":"ref_56","first-page":"37","article-title":"3D building model reconstruction from point clouds and ground plans","volume":"34","author":"Vosselman","year":"2001","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_57","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_58","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_59","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2016.01.010","article-title":"Closed-form solutions for estimating a rigid motion from plane correspondences extracted from point clouds","volume":"114","author":"Khoshelham","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Eslami, M., and Saadatseresht, M. (2020). A New Tie Plane-Based Method for Fine Registration of Imagery and Point Cloud Dataset. Can. J. Remote Sens., 1\u201318.","DOI":"10.1080\/07038992.2020.1785282"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Xu, Y., Boerner, R., Yao, W., Hoegner, L., and Stilla, U. (2017). Automatic coarse registration of point clouds in 3D urban scenes using voxel based plane constraint. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., 4.","DOI":"10.5194\/isprs-annals-IV-2-W4-185-2017"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/LGRS.2019.2910546","article-title":"3-D Deep Feature Construction for Mobile Laser Scanning Point Cloud Registration","volume":"16","author":"Zhang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_63","first-page":"51","article-title":"Correspondence matching in unorganized 3D point clouds using Convolutional Neural Networks","volume":"83","author":"Casas","year":"2019","journal-title":"Image Vis. Comput."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Charles, R.Q., Su, H., Kaichun, M., and Guibas, L.J. (2017, January 21\u201326). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.16"},{"key":"ref_65","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017). Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in Neural Information Processing Systems, NIPS."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Deng, H., Birdal, T., and Ilic, S. (2018). Ppf-foldnet: Unsupervised learning of rotation invariant 3d local descriptors. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-01228-1_37"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Aoki, Y., Goforth, H., Srivatsan, R.A., and Lucey, S. (2019, January 15\u201320). PointNetLK: Robust and efficient point cloud registration using PointNet. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00733"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3960","DOI":"10.1109\/LRA.2020.2970946","article-title":"CorsNet: 3D Point Cloud Registration by Deep Neural Network","volume":"5","author":"Kurobe","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.isprsjprs.2014.06.015","article-title":"Keypoint-based 4-Points Congruent Sets\u2013Automated marker-less registration of laser scans","volume":"96","author":"Theiler","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Zhou, Q.-Y., Park, J., and Koltun, V. (2016). Fast global registration. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46475-6_47"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Yang, J., Li, H., and Jia, Y. (2013, January 1\u20138). Go-ICP: Solving 3D Registration Efficiently and Globally Optimally. Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.184"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.autcon.2018.01.009","article-title":"SLAM-driven robotic mapping and registration of 3D point clouds","volume":"89","author":"Kim","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/TPAMI.2017.2717829","article-title":"Joint Alignment of Multiple Point Sets with Batch and Incremental Expectation-Maximization","volume":"40","author":"Evangelidis","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Myronenko, A., Song, X., and Carreira-Perpin\u00e1n, M.A. (2007). Non-rigid point set registration: Coherent point drift. Advances in Neural Information Processing Systems, NIPS.","DOI":"10.7551\/mitpress\/7503.003.0131"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1007\/s11432-011-4465-7","article-title":"A refined coherent point drift (CPD) algorithm for point set registration","volume":"54","author":"Wang","year":"2011","journal-title":"Sci. China Inf. Sci."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Lu, M., Zhao, J., Guo, Y., Ou, J., and Li, J. (2004, January 1\u20136). A 3D point cloud registration algorithm based on fast coherent point drift. Proceedings of the 2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, USA.","DOI":"10.1109\/AIPR.2014.7041917"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/34.121791","article-title":"A method for registration of 3-D shapes","volume":"14","author":"Besl","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_78","unstructured":"Chen, Y., and Medioni, G. (1991, January 9\u201311). Object modeling by registration of multiple range images. Proceedings of the 1991 IEEE International Conference on Robotics and Automation, Sacramento, CA, USA."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.neucom.2014.03.035","article-title":"LieTrICP: An improvement of trimmed iterative closest point algorithm","volume":"140","author":"Dong","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_80","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_81","doi-asserted-by":"crossref","unstructured":"Takeuchi, E., and Tsubouchi, T. (2006, January 9\u201315). A 3-D Scan Matching using Improved 3-D Normal Distributions Transform for Mobile Robotic Mapping. Proceedings of the 2006 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Beijing, China.","DOI":"10.1109\/IROS.2006.282246"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1177\/0278364912460895","article-title":"Fast and accurate scan registration through minimization of the distance between compact 3D NDT representations","volume":"31","author":"Stoyanov","year":"2012","journal-title":"Int. J. Robot. Res."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1177\/0278364914539404","article-title":"Scan registration using segmented region growing NDT","volume":"33","author":"Das","year":"2014","journal-title":"Int. J. Robot. Res."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1109\/LGRS.2019.2950128","article-title":"Density-Adaptive and Geometry-Aware Registration of TLS Point Clouds Based on Coherent Point Drift","volume":"17","author":"Zang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Zang, Y., Yang, B., Li, J., and Guan, H. (2019). An Accurate TLS and UAV Image Point Clouds Registration Method for Deformation Detection of Chaotic Hillside Areas. Remote Sens., 11.","DOI":"10.3390\/rs11060647"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/12\/759\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:47:30Z","timestamp":1760179650000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/12\/759"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,19]]},"references-count":85,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["ijgi9120759"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9120759","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2020,12,19]]}}}