{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T05:49:10Z","timestamp":1772171350525,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,12,7]],"date-time":"2020-12-07T00:00:00Z","timestamp":1607299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100015491","name":"Shanghai Institute of Technical Physics, Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["X-209"],"award-info":[{"award-number":["X-209"]}],"id":[{"id":"10.13039\/501100015491","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Rigid registration of 3D point clouds is the key technology in robotics and computer vision. Most commonly, the iterative closest point (ICP) and its variants are employed for this task. These methods assume that the closest point is the corresponding point and lead to sensitivity to the outlier and initial pose, while they have poor computational efficiency due to the closest point computation. Most implementations of the ICP algorithm attempt to deal with this issue by modifying correspondence or adding coarse registration. However, this leads to sacrificing the accuracy rate or adding the algorithm complexity. This paper proposes a hierarchical optimization approach that includes improved voxel filter and Multi-Scale Voxelized Generalized-ICP (MVGICP) for 3D point cloud registration. By combining traditional voxel sampling with point density, the outlier filtering and downsample are successfully realized. Through multi-scale iteration and avoiding closest point computation, MVGICP solves the local minimum problem and optimizes the operation efficiency. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of outlier filtering and registration performance.<\/jats:p>","DOI":"10.3390\/s20236999","type":"journal-article","created":{"date-parts":[[2020,12,7]],"date-time":"2020-12-07T21:37:42Z","timestamp":1607377062000},"page":"6999","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Hierarchical Optimization of 3D Point Cloud Registration"],"prefix":"10.3390","volume":"20","author":[{"given":"Huikai","family":"Liu","sequence":"first","affiliation":[{"name":"Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Linjian","family":"Lei","sequence":"additional","affiliation":[{"name":"Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China"}]},{"given":"Hui","family":"Xie","sequence":"additional","affiliation":[{"name":"Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Shengli","family":"Sun","sequence":"additional","affiliation":[{"name":"Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wolff, K., Kim, C., Zimmer, H., Schroers, C., Botsch, M., Sorkine-Hornung, O., and Sorkine-Hornung, A. (2016, January 25\u201328). Point cloud noise and outlier removal for image-based 3D reconstruction. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.20"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Drost, B., Ulrich, M., Navab, N., and Ilic, S. (2010, January 13\u201318). Model globally, match locally: Efficient and robust 3D object recognition. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540108"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1007\/s10846-017-0765-5","article-title":"Map-matching algorithms for robot self-localization: A comparison between perfect match, iterative closest point and normal distributions transform","volume":"93","author":"Sobreira","year":"2019","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_4","first-page":"586","article-title":"Method for registration of 3-D shapes","volume":"Volume 1611","author":"Besl","year":"1992","journal-title":"Sensor Fusion IV: Control Paradigms and Data Structures"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., and Cousins, S. (2011, January 9\u201313). 3D is here: Point Cloud Library (PCL). Proceedings of the IEEE International Conference on Robotics Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980567"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1016\/j.robot.2008.08.005","article-title":"Towards 3D point cloud based object maps for household environments","volume":"56","author":"Rusu","year":"2008","journal-title":"Rob. Autom. Syst."},{"key":"ref_7","first-page":"1034","article-title":"Outliers detection method based on dynamic standard deviation threshold using neighborhood density constraints for three dimensional point cloud","volume":"30","author":"Yang","year":"2018","journal-title":"J. Comput. Aided Des. Comput. Graph."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ramaswamy, S., Rastogi, R., and Shim, K. (2000, January 16\u201318). Efficient algorithms for mining outliers from large data sets. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Houston, TX, USA.","DOI":"10.1145\/342009.335437"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40965-018-0056-5","article-title":"Implementation and assessment of two density-based outlier detection methods over large spatial point clouds","volume":"3","author":"Pirotti","year":"2018","journal-title":"Open Geospat. Data Softw. Stand."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1109\/TMM.2014.2316145","article-title":"An accurate and robust range image registration algorithm for 3D object modeling","volume":"16","author":"Guo","year":"2014","journal-title":"IEEE Trans. Multimedia"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neucom.2017.04.015","article-title":"Multi-attribute statistics histograms for accurate and robust pairwise registration of range images","volume":"251","author":"Yang","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.agrformet.2016.05.005","article-title":"Efficient registration of terrestrial LiDAR scans using a coarse-to-fine strategy for forestry applications","volume":"225","author":"Zhang","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yuan, C., Yu, X., and Luo, Z. (2016, January 11\u201312). 3D point cloud matching based on principal component analysis and iterative closest point algorithm. Proceedings of the 2016 International Conference on Audio, Language and Image Processing (ICALIP), Shanghai, China.","DOI":"10.1109\/ICALIP.2016.7846655"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103247","DOI":"10.1016\/j.autcon.2020.103247","article-title":"Temporal comparison of construction sites using photogrammetric point cloud sequences and robust phase correlation","volume":"117","author":"Huang","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Aiger, D., Mitra, N.J., and Cohen-Or, D. (2008). 4-points congruent sets for robust pairwise surface registration. ACM SIGGRAPH 2008 Papers, ACM.","DOI":"10.1145\/1399504.1360684"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"283","DOI":"10.5194\/isprsannals-II-5-W2-283-2013","article-title":"Markerless point cloud registration with keypoint-based 4-points congruent sets","volume":"1","author":"Theiler","year":"2013","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1111\/cgf.12446","article-title":"Super 4pcs fast global pointcloud registration via smart indexing","volume":"Volume 33","author":"Mellado","year":"2014","journal-title":"Computer Graphics Forum"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.isprsjprs.2019.02.015","article-title":"Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets","volume":"151","author":"Xu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Frome, A., Huber, D., Kolluri, R., B\u00fclow, T., and Malik, J. (2004, January 11\u201314). Recognizing objects in range data using regional point descriptors. Proceedings of the European Conference on Computer Vision, Prague, Czech.","DOI":"10.1007\/978-3-540-24672-5_18"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Petrelli, A., and Di Stefano, L. (2011, January 6\u201313). On the repeatability of the local reference frame for partial shape matching. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126503"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tombari, F., Salti, S., and Di Stefano, L. (2010, January 5\u201311). Unique signatures of histograms for local surface description. Proceedings of the European Conference on Computer Vision, Crete, Greece.","DOI":"10.1007\/978-3-642-15558-1_26"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","unstructured":"Flint, A., Dick, A., and Van Den Hengel, A. (2007, January 3\u20135). Thrift: Local 3d structure recognition. Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007), Glenelg, Australia.","DOI":"10.1109\/DICTA.2007.4426794"},{"key":"ref_24","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_25","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_26","doi-asserted-by":"crossref","first-page":"2530","DOI":"10.1109\/TGRS.2019.2952086","article-title":"PLADE: A Plane-Based Descriptor for Point Cloud Registration With Small Overlap","volume":"58","author":"Chen","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.isprsjprs.2017.03.012","article-title":"SigVox\u2013A 3D feature matching algorithm for automatic street object recognition in mobile laser scanning point clouds","volume":"128","author":"Wang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1109\/ICPR.2002.1047997","article-title":"The trimmed iterative closest point algorithm","volume":"Volume 3","author":"Chetverikov","year":"2002","journal-title":"Object Recognition Supported by User Interaction for Service Robots"},{"key":"ref_29","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 IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.184"},{"key":"ref_30","unstructured":"Biber, P., and Stra\u00dfer, W. (2003, January 27\u201331). The normal distributions transform: A new approach to laser scan matching. Proceedings of the 2003 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No. 03CH37453), Las Vegas, NV, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0262-8856(92)90066-C","article-title":"Object modeling by registration of multiple range images","volume":"10","author":"Chen","year":"2002","journal-title":"Image Vision Comput."},{"key":"ref_32","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_33","first-page":"435","article-title":"Generalized-icp","volume":"2","author":"Segal","year":"2009","journal-title":"Robotics: Science and Systems"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bouaziz, S., Tagliasacchi, A., and Pauly, M. (2013). Sparse Iterative Closest Point, Computer Graphics Forum; Wiley Online Library.","DOI":"10.1111\/cgf.12178"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Koide, K., Yokozuka, M., Oishi, S., and Banno, A. (2020). Voxelized GICP for Fast and Accurate 3D Point Cloud Registration, EasyChair. Technical Report.","DOI":"10.1109\/ICRA48506.2021.9560835"},{"key":"ref_36","unstructured":"Korn, M., Holzkothen, M., and Pauli, J. (2014, January 5\u20138). Color supported generalized-ICP. Proceedings of the 2014 International Conference on Computer Vision Theory and Applications (VISAPP), Lisbon, Portugal."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Aoki, Y., Goforth, H., Srivatsan, R.A., and Lucey, S. (2019, January 15\u201320). Pointnetlk: Robust & 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_38","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Solomon, J.M. (2019, January 15\u201320). Deep closest point: Learning representations for point cloud registration. Proceedings of the IEEE International Conference on Computer Vision, Long Beach, CA, USA.","DOI":"10.1109\/ICCV.2019.00362"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1584","DOI":"10.1109\/TPAMI.2006.213","article-title":"Three-dimensional model-based object recognition and segmentation in cluttered scenes","volume":"28","author":"Mian","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/23\/6999\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:42:01Z","timestamp":1760179321000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/23\/6999"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,7]]},"references-count":39,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["s20236999"],"URL":"https:\/\/doi.org\/10.3390\/s20236999","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,7]]}}}