{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T13:26:22Z","timestamp":1772198782525,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T00:00:00Z","timestamp":1637712000000},"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":["U1711267,41671400,41971356,41701446"],"award-info":[{"award-number":["U1711267,41671400,41971356,41701446"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&amp;D Program of China","award":["2017YCF0601500,2017YFC0601504"],"award-info":[{"award-number":["2017YCF0601500,2017YFC0601504"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rigid registration of 3D indoor scenes is a fundamental yet vital task in various fields that include remote sensing (e.g., 3D reconstruction of indoor scenes), photogrammetry measurement, geometry modeling, etc. Nevertheless, state-of-the-art registration approaches still have defects when dealing with low-quality indoor scene point clouds derived from consumer-grade RGB-D sensors. The major challenge is accurately extracting correspondences between a pair of low-quality point clouds when they contain considerable noise, outliers, or weak texture features. To solve the problem, we present a point cloud registration framework in view of RGB-D information. First, we propose a point normal filter for effectively removing noise and simultaneously maintaining sharp geometric features and smooth transition regions. Second, we design a correspondence extraction scheme based on a novel descriptor encoding textural and geometry information, which can robustly establish dense correspondences between a pair of low-quality point clouds. Finally, we propose a point-to-plane registration technology via a nonconvex regularizer, which can further diminish the influence of those false correspondences and produce an exact rigid transformation between a pair of point clouds. Compared to existing state-of-the-art techniques, intensive experimental results demonstrate that our registration framework is excellent visually and numerically, especially for dealing with low-quality indoor scenes.<\/jats:p>","DOI":"10.3390\/rs13234755","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4755","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4832-5296","authenticated-orcid":false,"given":"Saishang","family":"Zhong","sequence":"first","affiliation":[{"name":"State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China"},{"name":"School of Earth Resources, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4097-4814","authenticated-orcid":false,"given":"Mingqiang","family":"Guo","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"},{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China"}]},{"given":"Ruina","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Jianguo","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China"},{"name":"School of Earth Resources, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Zhong","family":"Xie","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"},{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Zheng","family":"Liu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"},{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pavan, N.L., dos Santos, D.R., and Khoshelham, K. (2020). Global registration of terrestrial laser scanner point clouds using plane-to-plane correspondences. Remote Sens., 12.","DOI":"10.3390\/rs12071127"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cui, Y., Chang, W., N\u00f6ll, T., and Stricker, D. (2012, January 5\u20139). KinectAvatar: Fully automatic body capture using a single kinect. Proceedings of the Asian Conference on Computer Vision, Daejeon, Korea.","DOI":"10.1007\/978-3-642-37484-5_12"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"(2015). Globally consistent registration of terrestrial laser scans via graph optimization. ISPRS J. Photogramm. Remote Sens., 109, 126\u2013138.","DOI":"10.1016\/j.isprsjprs.2015.08.007"},{"key":"ref_4","first-page":"586","article-title":"Method for registration of 3-D shapes","volume":"Volume 1611","author":"Besl","year":"1992","journal-title":"Proceedings of the Sensor Fusion IV: Control Paradigms and data Structures"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohi, P., Shotton, J., Hodges, S., and Fitzgibbon, A. (2011, January 26\u201329). KinectFusion: Real-time dense surface mapping and tracking. Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality, Basel, Switzerland.","DOI":"10.1109\/ISMAR.2011.6092378"},{"key":"ref_6","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_7","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_8","unstructured":"Gelfand, N., Mitra, N.J., Guibas, L.J., and Pottmann, H. (2005, January 4\u20136). Robust Global Registration. Proceedings of the Third Eurographics Symposium on Geometry Processing, Vienna, Austria."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D., and Burgard, W. (2012, January 14\u201318). An evaluation of the RGB-D SLAM system. Proceedings of the 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA.","DOI":"10.1109\/ICRA.2012.6225199"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Henry, P., Krainin, M., Herbst, E., Ren, X., and Fox, D. (2014). RGB-D mapping: Using depth cameras for dense 3D modeling of indoor environments. Experimental Robotics, Springer.","DOI":"10.1007\/978-3-642-28572-1_33"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Guo, M., Wu, L., Huang, Y., and Chen, X. (2021). An efficient internet map tiles rendering approach on high resolution devices. J. Spat. Sci., 1\u201319.","DOI":"10.1080\/14498596.2021.1896394"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"22225","DOI":"10.1109\/ACCESS.2020.2969360","article-title":"Robust Rigid Registration Algorithm Based on Correntropy and Bi-Directional Distance","volume":"8","author":"Wan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","unstructured":"Wan, T., Du, S., Cui, W., Yao, R., Ge, Y., Li, C., Gao, Y., and Zheng, N. (2021). RGB-D Point Cloud Registration Based on Salient Object Detection. IEEE Trans. Neural Netw. Learn. Syst., 1\u201313."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1360612.1360684","article-title":"4-points congruent sets for robust pairwise surface registration","volume":"27","author":"Aiger","year":"2008","journal-title":"ACM Trans. Graph."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Zhang, L., Guo, J., Cheng, Z., Xiao, J., and Zhang, X. (2021). Efficient Pairwise 3-D Registration of Urban Scenes via Hybrid Structural Descriptors. IEEE Trans. Geosci. Remote. Sens., 1\u201317.","DOI":"10.1109\/TGRS.2021.3091380"},{"key":"ref_18","first-page":"2","article-title":"Real-time camera tracking and 3D reconstruction using signed distance functions","volume":"Volume 2","author":"Bylow","year":"2013","journal-title":"Robotics: Science and systems (RSS)"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pavlov, A.L., Ovchinnikov, G.W., Derbyshev, D.Y., Tsetserukou, D., and Oseledets, I.V. (2018, January 21\u201325). AA-ICP: Iterative closest point with Anderson acceleration. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8461063"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, J., Yao, Y., and Deng, B. (2021). Fast and Robust Iterative Closest Point. IEEE Trans. Pattern Anal. Mach. Intell., 1.","DOI":"10.1109\/TPAMI.2021.3054619"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.imavis.2004.05.007","article-title":"Robust Euclidean alignment of 3D point sets: The trimmed iterative closest point algorithm","volume":"23","author":"Chetverikov","year":"2005","journal-title":"Image Vis. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1111\/cgf.12178","article-title":"Sparse iterative closest point","volume":"Volume 32","author":"Bouaziz","year":"2013","journal-title":"Computer Graphics Forum"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhou, Q.Y., Park, J., and Koltun, V. (2016, January 8\u201316). Fast global registration. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_47"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.patcog.2019.03.013","article-title":"Correntropy based scale ICP algorithm for robust point set registration","volume":"93","author":"Wu","year":"2019","journal-title":"Pattern Recognit."},{"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 IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.184"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Feng, W., Zhang, J., Cai, H., Xu, H., Hou, J., and Bao, H. (2021, January 19\u201325). Recurrent Multi-view Alignment Network for Unsupervised Surface Registration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01016"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, X., Pontes, J.K., and Lucey, S. (2021, January 19\u201325). PointNetLK Revisited. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01257"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Solomon, J.M. (2019, January 27\u201328). Deep closest point: Learning representations for point cloud registration. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00362"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Guo, M., Liu, H., Xu, Y., and Huang, Y. (2020). Building extraction based on U-Net with an attention block and multiple losses. Remote Sens., 12.","DOI":"10.3390\/rs12091400"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Guo, M., Yu, Z., Xu, Y., Huang, Y., and Li, C. (2021). ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data. Remote Sens., 13.","DOI":"10.3390\/rs13071292"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1109\/TVCG.2012.310","article-title":"Registration of 3D point clouds and meshes: A survey from rigid to nonrigid","volume":"19","author":"Tam","year":"2012","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1857907.1857911","article-title":"\u21131-sparse reconstruction of sharp point set surfaces","volume":"29","author":"Avron","year":"2010","journal-title":"ACM Trans. Graph. TOG"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.cagd.2015.03.011","article-title":"Denoising point sets via L0 minimization","volume":"35\u201336","author":"Sun","year":"2015","journal-title":"Comput. Aided Geom. Des."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e1827","DOI":"10.1002\/cav.1827","article-title":"Mesh denoising via total variation and weighted Laplacian regularizations","volume":"29","author":"Zhong","year":"2018","journal-title":"Comput. Animat. Virtual Worlds"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhong, S., Xie, Z., Liu, J., and Liu, Z. (2019). Robust Mesh Denoising via Triple Sparsity. Sensors, 19.","DOI":"10.3390\/s19051001"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.cagd.2019.04.013","article-title":"A novel anisotropic second order regularization for mesh denoising","volume":"71","author":"Liu","year":"2019","journal-title":"Comput. Aided Geom. Des."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"102858","DOI":"10.1016\/j.cad.2020.102858","article-title":"Mesh Denoising via a Novel Mumford\u2013Shah Framework","volume":"126","author":"Liu","year":"2020","journal-title":"Comput. Aided Des."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Guo, M., Song, Z., Han, C., Zhong, S., Lv, R., and Liu, Z. (2021). Mesh denoising via adaptive consistent neighborhood. Sensors, 21.","DOI":"10.3390\/s21020412"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103088","DOI":"10.1016\/j.cad.2021.103088","article-title":"Shape-aware Mesh Normal Filtering","volume":"140","author":"Zhong","year":"2021","journal-title":"Comput. Aided Des."},{"key":"ref_40","unstructured":"Liu, Z., Li, Y., Wang, W., Liu, L., and Chen, R. (2021). Mesh Total Generalized Variation for Denoising. IEEE Trans. Vis. Comput. Graph., 1."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"102857","DOI":"10.1016\/j.cad.2020.102857","article-title":"A feature-preserving framework for point cloud denoising","volume":"127","author":"Liu","year":"2020","journal-title":"Comput. Aided Des."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Raguram, R., Frahm, J.M., and Pollefeys, M. (2008, January 12\u201318). A comparative analysis of RANSAC techniques leading to adaptive real-time random sample consensus. Proceedings of the European Conference on Computer Vision, Marseille, France.","DOI":"10.1007\/978-3-540-88688-4_37"},{"key":"ref_43","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_44","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_45","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.cagd.2015.03.022","article-title":"Efficient Sparse Icp","volume":"35","author":"Mavridis","year":"2015","journal-title":"Comput. Aided Geom. Des."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3306346.3323037","article-title":"A symmetric objective function for ICP","volume":"38","author":"Rusinkiewicz","year":"2019","journal-title":"ACM Trans. Graph. TOG"},{"key":"ref_47","first-page":"1","article-title":"Representing attitude: Euler angles, unit quaternions, and rotation vectors","volume":"58","author":"Diebel","year":"2006","journal-title":"Matrix"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.neucom.2015.10.104","article-title":"Dense 3D reconstruction combining depth and RGB information","volume":"175","author":"Pan","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Handa, A., Whelan, T., McDonald, J., and Davison, A.J. (June, January 31). A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6907054"},{"key":"ref_50","unstructured":"Dai, W., Zhang, Y., Li, P., Fang, Z., and Scherer, S. (2020). RGB-D SLAM in Dynamic Environments Using Point Correlations. IEEE Trans. Pattern Anal. Mach. Intell., 1."},{"key":"ref_51","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_52","doi-asserted-by":"crossref","first-page":"232:1","DOI":"10.1145\/2980179.2980232","article-title":"Mesh Denoising via Cascaded Normal Regression","volume":"35","author":"Wang","year":"2016","journal-title":"ACM Trans. Graph."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1111\/j.1467-8659.2009.01388.x","article-title":"Feature preserving point set surfaces based on non-linear kernel regression","volume":"Volume 28","author":"Guennebaud","year":"2009","journal-title":"Computer Graphics Forum"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1111\/cgf.13068","article-title":"Point cloud denoising via moving RPCA","volume":"Volume 36","author":"Mattei","year":"2017","journal-title":"Computer Graphics Forum"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., and Van Gool, L. (2006, January 7\u201313). Surf: Speeded up robust features. Proceedings of the European Conference on Computer Vision, Graz, Austria.","DOI":"10.1007\/11744023_32"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/BF00131148","article-title":"On the unification of line processes, outlier rejection, and robust statistics with applications in early vision","volume":"19","author":"Black","year":"1996","journal-title":"Int. J. Comput. Vis."},{"key":"ref_57","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_58","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":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_59","unstructured":"Handa, A., Whelan, T., McDonald, J.B., and Davison, A.J. (2021, September 23). The ICL-NUIM Dataset. Available online: http:\/\/www.doc.ic.ac.uk\/~ahanda\/VaFRIC\/iclnuim.html."},{"key":"ref_60","unstructured":"Sturm, J., Engelhard, N., Endres, F., Burgard, W., and Cremers, D. (2021, September 23). The TUM Dataset. Available online: https:\/\/vision.in.tum.de\/data\/datasets\/rgbd-dataset\/download."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4755\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:35:13Z","timestamp":1760168113000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4755"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,24]]},"references-count":60,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234755"],"URL":"https:\/\/doi.org\/10.3390\/rs13234755","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,24]]}}}