{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T21:45:48Z","timestamp":1725399948672},"reference-count":49,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,12,4]],"date-time":"2021-12-04T00:00:00Z","timestamp":1638576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"We propose an innovative method with which to extract building interior structure information automatically, including ceiling, floor, and wall. Our approach outperforms previous methods in the following respects. First, we propose an approach based on principal component analysis (PCA) to find the ground plane, which is regarded as the new Cartesian plane. Second, to reduce the complexity of data processing, the data are projected into two dimensions and transformed into a binary image via the operation of an improved radius outlier removal (ROR) filter. Third, a traditional thinning algorithm is adopted to extract the image skeleton. Then, we propose a method for calculating slope through the nearest neighbor point. Moreover, the line is represented with the slopes to obtain information pertaining to the interior planes. Finally, the outline of the line is restored to a three-dimensional structure. The proposed method is evaluated in multiple scenarios, and the results show that the method is accurate (the maximum error of 0.03 m was in three scenarios) in indoor environments.<\/jats:p>","DOI":"10.3390\/rs13234930","type":"journal-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T08:10:38Z","timestamp":1638778238000},"page":"4930","source":"Crossref","is-referenced-by-count":9,"title":["Automatic Extraction of Indoor Structural Information from Point Clouds"],"prefix":"10.3390","volume":"13","author":[{"given":"Dongyang","family":"Cheng","sequence":"first","affiliation":[{"name":"School of Aeronautics and Astronautics, Central South University, Changsha 410083, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2243-0012","authenticated-orcid":false,"given":"Junchao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Central South University, Changsha 410083, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9286-1999","authenticated-orcid":false,"given":"Dangjun","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Central South University, Changsha 410083, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8639-9336","authenticated-orcid":false,"given":"Jianlai","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Central South University, Changsha 410083, China"}]},{"given":"Di","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Central South University, Changsha 410083, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The KITTI dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5119","DOI":"10.1109\/TIE.2015.2410258","article-title":"Development of Autonomous Car\u2014Part II: A Case Study on the Implementation of an Autonomous Driving System Based on Distributed Architecture","volume":"62","author":"Jo","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_3","first-page":"1768","article-title":"Autonomous-driving vehicle test technology based on virtual reality","volume":"2018","author":"Yao","year":"2018","journal-title":"J. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/MNET.2019.1900120","article-title":"Autonomous Driving Cars in Smart Cities: Recent Advances, Requirements, and Challenges","volume":"34","author":"Yaqoob","year":"2020","journal-title":"IEEE Netw."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1002\/rob.20255","article-title":"Autonomous driving in urban environments: Boss and the Urban Challenge","volume":"25","author":"Urmson","year":"2008","journal-title":"J. Field Robot."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gelbart, A., Weber, C., Bybee-Driscoll, S., Freeman, J., Fetzer, G.J., Seales, T., McCarley, K.A., and Wright, J. (2003, January 21). FLASH lidar data collections in terrestrial and ocean environments. Proceedings of the SPIE 5086, Laser Radar Technology and Applications VIII, Orlando, FL, USA.","DOI":"10.1117\/12.501595"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S1270-9638(99)80042-X","article-title":"Wake-vortex characteristics of military-type aircraft measured at Airport Oberpfaffenhofen using the DLR Laser Doppler Anemometer","volume":"3","author":"Kopp","year":"1999","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4653","DOI":"10.1109\/TGRS.2020.2965918","article-title":"Deep Convolutional Neural Network-Based Robust Phase Gradient Estimation for Two-Dimensional Phase Unwrapping Using SAR Interferograms","volume":"58","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MGRS.2021.3065811","article-title":"Artificial Intelligence In Interferometric Synthetic Aperture Radar Phase Unwrapping: A Review","volume":"9","author":"Zhou","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1712","DOI":"10.2514\/1.28338","article-title":"Flight Test Evaluation of a Helicopter Airborne Lidar","volume":"44","author":"Matayoshi","year":"2007","journal-title":"J. Aircr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"392","DOI":"10.2514\/1.44950","article-title":"Airborne Lidar for Automatic Feedforward Control of Turbulent In-Flight Phenomena","volume":"47","author":"Rabadan","year":"2010","journal-title":"J. Aircr."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4984","DOI":"10.1038\/s41467-019-12943-7","article-title":"Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers","volume":"10","author":"Tachella","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6282","DOI":"10.1016\/j.ijleo.2014.08.016","article-title":"LiDAR data reduction assisted by optical image for 3D building reconstruction","volume":"125","author":"Yang","year":"2014","journal-title":"Optik"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, H.Y., Yang, C., Wang, Z., Wu, G.L., Li, W.H., and Liu, C. (2012, January 22\u201327). A Hierarchical Contour Method for Automatic 3d City Reconstruction From Lidar Data. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6350870"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1654","DOI":"10.1109\/LGRS.2014.2314179","article-title":"Automatic Registration of Tree Point Clouds From Terrestrial LiDAR Scanning for Reconstructing the Ground Scene of Vegetated Surfaces","volume":"11","author":"Guiyun","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.autcon.2012.03.001","article-title":"Numerical modeling of LiDAR-based geological model for landslide analysis","volume":"24","author":"Hu","year":"2012","journal-title":"Autom. Constr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5679","DOI":"10.3390\/s90705679","article-title":"Object-Based Integration of Photogrammetric and LiDAR Data for Automated Generation of Complex Polyhedral Building Models","volume":"9","author":"Kim","year":"2009","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.isprsjprs.2013.10.003","article-title":"Automatic representation and reconstruction of DBM from LiDAR data using Recursive Minimum Bounding Rectangle","volume":"93","author":"Kwak","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1017\/S037346331400054X","article-title":"A LiDAR-Aided Indoor Navigation System for UGVs","volume":"68","author":"Liu","year":"2014","journal-title":"J. Navig."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, Y., Fan, B., Meng, G., Lu, J., Xiang, S., and Pan, C. (November, January 27). DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00534"},{"key":"ref_21","first-page":"338","article-title":"Survey on 3D Surface Reconstruction","volume":"12","author":"Khatamian","year":"2016","journal-title":"J. Inf. Process. Syst."},{"key":"ref_22","first-page":"150","article-title":"A survey of point cloud reconstruction methods","volume":"3","author":"Matiukas","year":"2008","journal-title":"Electr. Control Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5705","DOI":"10.3390\/s120505705","article-title":"Automatic construction of 3D basic-semantic models of inhabited interiors using laser scanners and RFID sensors","volume":"12","author":"Valero","year":"2012","journal-title":"Sensors"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shan, T.X., and Englot, B. (2018, January 1\u20135). LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594299"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Shan, T., Englot, B., Meyers, D., Wang, W., Ratti, C., and Rus, D. (January, January 24). LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341176"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.cag.2015.07.008","article-title":"Automatic reconstruction of parametric building models from indoor point clouds","volume":"54","author":"Ochmann","year":"2016","journal-title":"Comput. Graph."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.isprsjprs.2019.03.017","article-title":"Automatic reconstruction of fully volumetric 3D building models from oriented point clouds","volume":"151","author":"Ochmann","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1111\/j.1467-8659.2007.01016.x","article-title":"Efficient RANSAC for point-cloud shape detection","volume":"26","author":"Schnabel","year":"2007","journal-title":"Comput. Graph. Forum."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.asoc.2015.05.007","article-title":"Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC","volume":"34","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LRA.2017.2651939","article-title":"Automatic Room Segmentation From Unstructured 3-D Data of Indoor Environments","volume":"2","author":"Ambrus","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sanchez, V., and Zakhor, A. (October, January 30). Planar 3d Modeling of Building Interiors From Point Cloud Data. Proceedings of the 2012 IEEE International Conference on Image Processing (ICIP 2012), Orlando, FL, USA.","DOI":"10.1109\/ICIP.2012.6467225"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.autcon.2015.04.001","article-title":"Automatic BIM component extraction from point clouds of existing buildings for sustainability applications","volume":"56","author":"Wang","year":"2015","journal-title":"Autom. Constr."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.cag.2014.07.005","article-title":"Automatic room detection and reconstruction in cluttered indoor environments with complex room layouts","volume":"44","author":"Mura","year":"2014","journal-title":"Comput. Graph."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.isprsjprs.2014.02.004","article-title":"Indoor scene reconstruction using feature sensitive primitive extraction and graph-cut","volume":"90","author":"Oesau","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"S1255","DOI":"10.1088\/0264-9381\/22\/18\/S39","article-title":"Adaptive Hough transform for the search of periodic sources","volume":"22","author":"Palomba","year":"2005","journal-title":"Class. Quant. Grav."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103460","DOI":"10.1016\/j.autcon.2020.103460","article-title":"Automatic structural mapping and semantic optimization from indoor point clouds","volume":"124","author":"Wu","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, C., and Cho, Y. (2012, January 21\u201323). Automated 3D building envelope recognition from point clouds for energy analysis. Proceedings of the Construction Research Congress 2012: Construction Challenges in a Flat World, West Lafayette, IN, USA.","DOI":"10.1061\/9780784412329.116"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.autcon.2012.10.006","article-title":"Automatic creation of semantically rich 3D building models from laser scanner data","volume":"31","author":"Xiong","year":"2013","journal-title":"Autom. Constr."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tang, S., Zhang, Y., Li, Y., Yuan, Z., Wang, Y., Zhang, X., Li, X., Zhang, Y., Guo, R., and Wang, W. (2019). Fast and Automatic Reconstruction of Semantically Rich 3D Indoor Maps from Low-quality RGB-D Sequences. Sensors, 19.","DOI":"10.3390\/s19030533"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Tang, S., Zhu, Q., Chen, W., Darwish, W., Wu, B., Hu, H., and Chen, M. (2016). Enhanced RGB-D Mapping Method for Detailed 3D Indoor and Outdoor Modeling. Sensors, 16.","DOI":"10.3390\/s16101589"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Stojanovic, V., Trapp, M., Richter, R., and D\u00f6llner, J. (2018, January 20\u201322). A service-oriented approach for classifying 3D points clouds by example of office furniture classification. Proceedings of the 23rd International ACM Conference on 3D Web Technology, Pozna\u0144, Poland.","DOI":"10.1145\/3208806.3208810"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"04015074","DOI":"10.1061\/(ASCE)CP.1943-5487.0000556","article-title":"Automated 3D Wireframe Modeling of Indoor Structures from Point Clouds Using Constrained Least-Squares Adjustment for As-Built BIM","volume":"30","author":"Jung","year":"2016","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Jung, J., Stachniss, C., and Kim, C. (2017). Automatic Room Segmentation of 3D Laser Data Using Morphological Processing. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6070206"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"317","DOI":"10.2478\/v10006-010-0024-4","article-title":"K3M: A universal algorithm for image skeletonization and a review of thinning techniques","volume":"20","author":"Saeed","year":"2010","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1145\/357994.358023","article-title":"A fast parallel algorithm for thinning digital patterns","volume":"27","author":"Zhang","year":"1984","journal-title":"Commun. ACM"},{"key":"ref_46","unstructured":"Harris, C., and Stephens, M. (September, January 31). A combined corner and edge detector. Proceedings of the Alvey Vision Conference, Manchester, UK."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1023\/A:1007963824710","article-title":"SUSAN\u2014A new approach to low level image processing","volume":"23","author":"Smith","year":"1997","journal-title":"Int. J. Comput. Vis."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.neucom.2015.01.102","article-title":"A KD curvature based corner detector","volume":"173","author":"Chen","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3788\/LOP55.011008","article-title":"Point cloud denoising and simplification algorithm based on method library","volume":"55","author":"Renzhong","year":"2018","journal-title":"Laser Optoelectron. Prog."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4930\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T12:34:09Z","timestamp":1721651649000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4930"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,4]]},"references-count":49,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234930"],"URL":"https:\/\/doi.org\/10.3390\/rs13234930","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,4]]}}}