{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T15:47:53Z","timestamp":1765295273428,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T00:00:00Z","timestamp":1641427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61960206010"],"award-info":[{"award-number":["61960206010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Science and Technology Planning Project","award":["2021YJ0080"],"award-info":[{"award-number":["2021YJ0080"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Establishing an effective local feature descriptor and using an accurate key point matching algorithm are two crucial tasks in recognizing and registering on the 3D point cloud. Because the descriptors need to keep enough descriptive ability against the effect of noise, occlusion, and incomplete regions in the point cloud, a suitable key point matching algorithm can get more precise matched pairs. To obtain an effective descriptor, this paper proposes a Multi-Statistics Histogram Descriptor (MSHD) that combines spatial distribution and geometric attributes features. Furthermore, based on deep learning, we developed a new key point matching algorithm that could identify more corresponding point pairs than the existing methods. Our method is evaluated based on Stanford 3D dataset and four real component point cloud dataset from the train bottom. The experimental results demonstrate the superiority of MSHD because its descriptive ability and robustness to noise and mesh resolution are greater than those of carefully selected baselines (e.g., FPFH, SHOT, RoPS, and SpinImage descriptors). Importantly, it has been confirmed that the error of rotation and translation matrix is much smaller based on our key point matching algorithm, and the precise corresponding point pairs can be captured, resulting in enhanced recognition and registration for three-dimensional surface matching.<\/jats:p>","DOI":"10.3390\/s22020417","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:08:26Z","timestamp":1641769706000},"page":"417","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Matching Algorithm for 3D Point Cloud Recognition and Registration Based on Multi-Statistics Histogram Descriptors"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2724-7807","authenticated-orcid":false,"given":"Jinlong","family":"Li","sequence":"first","affiliation":[{"name":"School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6063-2793","authenticated-orcid":false,"given":"Bingren","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaorong","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,6]]},"reference":[{"key":"ref_1","first-page":"985","article-title":"Research progress in three-dimensional object recognition","volume":"5","author":"L","year":"2000","journal-title":"J. Image Graph."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Huang, Z., Yu, Y., Xu, J., Ni, F., and Le, X. (2020, January 13\u201319). PF-Net: Point Fractal Network for 3D Point Cloud Completion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00768"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, H., and Hartley, R. (2007, January 14\u201321). The 3D-3D Registration Problem Revisited. Proceedings of the 2007 IEEE 11th International Conference on Computer Vision, Rio de Janeiro, Brazil.","DOI":"10.1109\/ICCV.2007.4409077"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/s11263-015-0824-y","article-title":"A Comprehensive Performance Evaluation of 3D Local Feature Descriptors","volume":"116","author":"Guo","year":"2016","journal-title":"Int. J. Comput. Vis."},{"key":"ref_6","unstructured":"Johnson, A.E. (1997). Spin-Images: A Representation for 3-D Surface Matching. [Ph.D. Thesis, Robotics Institute, Carnegie Mellon University]."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Halma, A., Haar, F.T., Bovenkamp, E., Eendebak, P., and Eekeren, A.V. (2010, January 25). Single spin image-ICP matching for efficient 3D object recognition. Proceedings of the ACM Workshop on 3D Object Retrieval, Firenze, Italy.","DOI":"10.1145\/1877808.1877814"},{"key":"ref_8","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 IEEE International Conference on Robotics & Automation, Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152473"},{"key":"ref_9","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 Conference on Computer Vision, Crete, Greece.","DOI":"10.1007\/978-3-642-15558-1_26"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s10015-016-0265-x","article-title":"Point cloud matching using singular value decomposition","volume":"21","author":"Oomori","year":"2016","journal-title":"Artif. Life Robot."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tombari, F., Salti, S., and Stefano, L.D. (2011). Unique shape context for 3d data description. 3DOR 2010: Proceedings of the ACM Workshop on 3D Object Retrieval, ACM.","DOI":"10.1145\/1877808.1877821"},{"key":"ref_13","first-page":"17","article-title":"Intrinsic Spin Images: A subspace decomposition approach to understanding 3D deformable shapes","volume":"10","author":"Wang","year":"2010","journal-title":"Procdpvt"},{"key":"ref_14","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, Acropolis Convention Center, Nice, France.","DOI":"10.1109\/IROS.2008.4650967"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1016\/j.patrec.2007.02.009","article-title":"3D free-form object recognition in range images using local surface patches","volume":"28","author":"Chen","year":"2007","journal-title":"Pattern Recognit. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1111\/j.1467-8659.2009.01515.x","article-title":"A Concise and Provably Informative Multi-Scale Signature Based on Heat Diffusion","volume":"28","author":"Sun","year":"2009","journal-title":"Comput. Graph. Forum"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lu, B., and Wang, Y. (2019). Matching Algorithm of 3D Point Clouds Based on Multiscale Features and Covariance Matrix Descriptors. IEEE Access.","DOI":"10.1109\/ACCESS.2019.2943003"},{"key":"ref_18","unstructured":"Qi, C.R., Su, H., Mo, K., 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."},{"key":"ref_19","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017, January 4). PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. Proceedings of the NIPS\u201917: Proceedings of the 31st International Conference on Neural Information Processing Systems, Online."},{"key":"ref_20","unstructured":"Li, Y., Bu, R., Sun, M., and Chen, B. (2018, January 2\u20138). PointCNN. Proceedings of the 32nd Conference on Neural Information Processing Systems (NIPS), Montreal, Canada."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., and Li, F. (2019, January 15\u201320). PointConv: Deep Convolutional Networks on 3D Point Clouds. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00985"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.optlastec.2012.04.027","article-title":"An automatic registration algorithm for the scattered point clouds based on the curvature feature","volume":"46","author":"He","year":"2013","journal-title":"Opt. Laser Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1109\/TPAMI.2015.2513405","article-title":"Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration","volume":"38","author":"Yang","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","unstructured":"Hong, S., Ko, H., and Kim, J. (2012, January 3\u20137). VICP: Velocity Updating Iterative Closest Point Algorithm. Proceedings of the IEEE International Conference on Robotics & Automation, Anchorage, AK, USA."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Censi, A. (2008, January 19\u201323). An ICP variant using a point-to-line metric. Proceedings of the IEEE International Conference on Robotics & Automation, Pasadena, CA, USA.","DOI":"10.1109\/ROBOT.2008.4543181"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1002\/rob.20204","article-title":"Scan registration for autonomous mining vehicles using 3D-NDT","volume":"24","author":"Magnusson","year":"2010","journal-title":"J. Field Robot."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"102899.1","DOI":"10.1016\/j.cviu.2019.102899","article-title":"Graph-matching-based correspondence search for nonrigid point cloud registration","volume":"192","author":"Chang","year":"2020","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"012012","DOI":"10.1088\/1742-6596\/1634\/1\/012012","article-title":"Point Cloud Registration Algorithm Based on Overlapping Region Extraction","volume":"1634","author":"Li","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"He, Y., and Lee, C.H. (2020, January 20\u201323). An Improved ICP Registration Algorithm by Combining PointNet++ and ICP Algorithm. Proceedings of the 2020 6th International Conference on Control, Automation and Robotics (ICCAR), Singapore.","DOI":"10.1109\/ICCAR49639.2020.9108032"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"100120","DOI":"10.1109\/ACCESS.2020.2995369","article-title":"A Local Feature Descriptor Based on Rotational Volume for Pairwise Registration of Point Clouds","volume":"8","author":"Xiong","year":"2020","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1016\/j.cviu.2010.11.021","article-title":"Local shape descriptor selection for object recognition in range data","volume":"115","author":"Taati","year":"2011","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1177\/0278364911436019","article-title":"Rigid 3D geometry matching for grasping of known objects in cluttered scenes","volume":"31","author":"Papazov","year":"2012","journal-title":"Int. J. Robot. Res."},{"key":"ref_35","unstructured":"Yu, Z. (October, January 27). Intrinsic shape signatures: A shape descriptor for 3D object recognition. Proceedings of the IEEE International Conference on Computer Vision Workshops, Kyoto, Japan."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/417\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:36:33Z","timestamp":1760362593000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/417"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,6]]},"references-count":35,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22020417"],"URL":"https:\/\/doi.org\/10.3390\/s22020417","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,1,6]]}}}