{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T02:38:56Z","timestamp":1772678336425,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T00:00:00Z","timestamp":1672012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFB2600300"],"award-info":[{"award-number":["2021YFB2600300"]}]},{"name":"National Key Research and Development Program of China","award":["2021YFB2600303"],"award-info":[{"award-number":["2021YFB2600303"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Indoor scene point cloud segmentation plays an essential role in 3D reconstruction and scene classification. This paper proposes a multi-constraint graph clustering method (MCGC) for indoor scene segmentation. The MCGC method considers multi-constraints, including extracted structural planes, local surface convexity, and color information of objects for indoor segmentation. Firstly, the raw point cloud is partitioned into surface patches, and we propose a robust plane extraction method to extract the main structural planes of the indoor scene. Then, the match between the surface patches and the structural planes is achieved by global energy optimization. Next, we closely integrate multiple constraints mentioned above to design a graph clustering algorithm to partition cluttered indoor scenes into object parts. Finally, we present a post-refinement step to filter outliers. We conducted experiments on a benchmark RGB-D dataset and a real indoor laser-scanned dataset to perform numerous qualitative and quantitative evaluation experiments, the results of which have verified the effectiveness of the MCGC method. Compared with state-of-the-art methods, MCGC can deal with the segmentation of indoor scenes more efficiently and restore more details of indoor structures. The segment precision and the segment recall of experimental results reach 70% on average. In addition, a great advantage of the MCGC method is that the speed of processing point clouds is very fast; it takes about 1.38 s to segment scene data of 1 million points. It significantly reduces the computation overhead of scene point cloud data and achieves real-time scene segmentation.<\/jats:p>","DOI":"10.3390\/rs15010131","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T02:53:11Z","timestamp":1672109591000},"page":"131","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Indoor 3D Point Cloud Segmentation Based on Multi-Constraint Graph Clustering"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6648-2768","authenticated-orcid":false,"given":"Ziwei","family":"Luo","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Zhong","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3048-1496","authenticated-orcid":false,"given":"Jie","family":"Wan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geological and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6776-6663","authenticated-orcid":false,"given":"Ziyin","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Lu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Liufeng","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7890","DOI":"10.1109\/TGRS.2020.2984943","article-title":"Indoor Point Cloud Segmentation Using Iterative Gaussian Mapping and Improved Model Fitting","volume":"58","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Macher, H., Landes, T., and Grussenmeyer, P. (2017). From Point Clouds to Building Information Models: 3D Semi-Automatic Reconstruction of Indoors of Existing Buildings. Appl. Sci., 7.","DOI":"10.3390\/app7101030"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107166","DOI":"10.1016\/j.compag.2022.107166","article-title":"3D point cloud density-based segmentation for vine rows detection and localisation","volume":"199","author":"Biglia","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Maturana, D., and Scherer, S. (October, January 28). VoxNet: A 3D Convolutional Neural Network for real-time object recognition. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.","DOI":"10.1109\/IROS.2015.7353481"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1016\/j.ins.2022.06.032","article-title":"Extraction of indoor objects based on the exponential function density clustering model","volume":"607","author":"Chen","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"04022087","DOI":"10.1061\/(ASCE)CO.1943-7862.0002345","article-title":"Geometric Modeling and Surface-Quality Inspection of Prefabricated Concrete Components Using Sliced Point Clouds","volume":"148","author":"Xu","year":"2022","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"04022025","DOI":"10.1061\/(ASCE)ME.1943-5479.0001055","article-title":"Deep Learning\u2013Based Automation of Scan-to-BIM with Modeling Objects from Occluded Point Clouds","volume":"38","author":"Park","year":"2022","journal-title":"J. Manag. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/s00371-017-1405-6","article-title":"A self-adaptive segmentation method for a point cloud","volume":"34","author":"Fan","year":"2017","journal-title":"Vis. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4160","DOI":"10.1109\/JSTARS.2019.2936662","article-title":"An Accurate and Robust Region-Growing Algorithm for Plane Segmentation of TLS Point Clouds Using a Multiscale Tensor Voting Method","volume":"12","author":"Wu","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"113439","DOI":"10.1016\/j.eswa.2020.113439","article-title":"Boundary constrained voxel segmentation for 3D point clouds using local geometric differences","volume":"157","author":"Saglam","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Li, L., Yang, F., Zhu, H., Li, D., Li, Y., and Tang, L. (2017). An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells. Remote. Sens., 9.","DOI":"10.3390\/rs9050433"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xu, B., Chen, Z., Zhu, Q., Ge, X., Huang, S., Zhang, Y., Liu, T., and Wu, D. (2022). Geometrical Segmentation of Multi-Shape Point Clouds Based on Adaptive Shape Prediction and Hybrid Voting RANSAC. Remote Sens., 14.","DOI":"10.3390\/rs14092024"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.ins.2022.04.006","article-title":"AF2GNN: Graph convolution with adaptive filters and aggregator fusion for hyperspectral image classification","volume":"602","author":"Ding","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yao, D., Zhi-Li, Z., Xiao-Feng, Z., Wei, C., Fang, H., Yao-Ming, C., and Cai, W.-W. Deep hybrid: Multi-graph neural network collaboration for hyperspectral image classification. Def. Technol., 2022. in press.","DOI":"10.1016\/j.dt.2022.02.007"},{"key":"ref_16","first-page":"5504205","article-title":"Graph Sample and Aggregate-Attention Network for Hyperspectral Image Classification","volume":"19","author":"Ding","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.neucom.2022.06.031","article-title":"Multi-feature fusion: Graph neural network and CNN combining for hyperspectral image classification","volume":"501","author":"Ding","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Golovinskiy, A., and Funkhouser, T. (October, January 27). Min-cut based segmentation of point clouds. Proceedings of the 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, Kyoto, Japan.","DOI":"10.1109\/ICCVW.2009.5457721"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s11263-006-7934-5","article-title":"Graph Cuts and Efficient N-D Image Segmentation","volume":"70","author":"Boykov","year":"2006","journal-title":"Int. J. Comput. Vis."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.isprsjprs.2014.04.022","article-title":"A global optimization approach to roof segmentation from airborne lidar point clouds","volume":"94","author":"Yan","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s11263-011-0474-7","article-title":"Energy-Based Geometric Multi-model Fitting","volume":"97","author":"Isack","year":"2011","journal-title":"Int. J. Comput. Vis."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, H., Wang, Z., Lin, L., Liang, H., Huang, W., and Xu, F. (2020). Two-Layer-Graph Clustering for Real-Time 3D LiDAR Point Cloud Segmentation. Appl. Sci., 10.","DOI":"10.3390\/app10238534"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.patrec.2017.12.016","article-title":"Voxel-based segmentation of 3D point clouds from construction sites using a probabilistic connectivity model","volume":"102","author":"Xu","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Papon, J., Abramov, A., Schoeler, M., and Worgotter, F. (2022, April 27). \u201cVoxel Cloud Connectivity Segmentation Supervoxels for Point Clouds,\u201d in CVPR13. Available online: https:\/\/openaccess.thecvf.com\/content_cvpr_2013\/html\/Papon_Voxel_Cloud_Connectivity_2013_CVPR_paper.html.","DOI":"10.1109\/CVPR.2013.264"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.isprsjprs.2018.05.004","article-title":"Toward better boundary preserved supervoxel segmentation for 3D point clouds","volume":"143","author":"Lin","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8492","DOI":"10.1080\/01431161.2021.1978583","article-title":"A novel 3D point cloud segmentation algorithm based on multi-resolution supervoxel and MGS","volume":"42","author":"Li","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","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_28","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. arXiv."},{"key":"ref_29","first-page":"5536716","article-title":"Unsupervised Self-correlated Learning Smoothy Enhanced Locality Preserving Graph Convolution Embedding Clustering for Hyperspectral Images","volume":"60","author":"Ding","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","first-page":"5511812","article-title":"Semi-Supervised Locality Preserving Dense Graph Neural Network with ARMA Filters and Context-Aware Learning for Hyperspectral Image Classification","volume":"60","author":"Ding","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","first-page":"5536016","article-title":"Self-Supervised Locality Preserving Low-Pass Graph Convolutional Embedding for Large-Scale Hyperspectral Image Clustering","volume":"60","author":"Ding","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4561","DOI":"10.1109\/JSTARS.2021.3074469","article-title":"Multiscale Graph Sample and Aggregate Network with Context-Aware Learning for Hyperspectral Image Classification","volume":"14","author":"Ding","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wan, J., Xie, Z., Xu, Y., Zeng, Z., Yuan, D., and Qiu, Q. (2021). DGANet: A Dilated Graph Attention-Based Network for Local Feature Extraction on 3D Point Clouds. Remote Sens., 13.","DOI":"10.3390\/rs13173484"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zeng, Z., Xu, Y., Xie, Z., Wan, J., Wu, W., and Dai, W. (2022). RG-GCN: A Random Graph Based on Graph Convolution Network for Point Cloud Semantic Segmentation. Remote. Sens., 14.","DOI":"10.3390\/rs14164055"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1080\/13658816.2022.2111572","article-title":"A geometry-aware attention network for semantic segmentation of MLS point clouds","volume":"37","author":"Wan","year":"2022","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_36","first-page":"102953","article-title":"LEARD-Net: Semantic segmentation for large-scale point cloud scene","volume":"112","author":"Zeng","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf. ITC J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1145\/3326362","article-title":"Dynamic Graph CNN for Learning on Point Clouds","volume":"38","author":"Wang","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, L., Huang, Y., Hou, Y., Zhang, S., and Shan, J. (2019, January 15\u201320). Graph Attention Convolution for Point Cloud Semantic Segmentation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01054"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, W., Yu, R., Huang, Q., and Neumann, U. (2019). SGPN: Similarity Group Proposal Network for 3D Point Cloud In-stance Segmentation. arXiv.","DOI":"10.1109\/CVPR.2018.00272"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2799","DOI":"10.1109\/LRA.2021.3062607","article-title":"LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud Segmentation","volume":"6","author":"Chen","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_41","unstructured":"Yang, B., Wang, J., Clark, R., Hu, Q., Wang, S., Markham, A., and Trigoni, N. (2019). Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Oh, S., Lee, D., Kim, M., Kim, T., and Cho, H. (2021). Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing. Remote Sens., 13.","DOI":"10.3390\/rs13020161"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, L., and Wang, Y. (2022). Slice-Guided Components Detection and Spatial Semantics Acquisition of Indoor Point Clouds. Sensors, 22.","DOI":"10.3390\/s22031121"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1109\/LGRS.2017.2647816","article-title":"Geometric Primitive Extraction from Point Clouds of Construction Sites Using VGS","volume":"14","author":"Xu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Runz, M., Buffier, M., and Agapito, L. (2018, January 16\u201320). MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects. Proceedings of the 2018 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Munich, Germany.","DOI":"10.1109\/ISMAR.2018.00024"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Pham, T.T., Eich, M., Reid, I., and Wyeth, G. (2016, January 9\u201314). Geometrically consistent plane extraction for dense indoor 3D maps segmentation. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Republic of Korea.","DOI":"10.1109\/IROS.2016.7759618"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.isprsjprs.2014.10.005","article-title":"Hierarchical extraction of urban objects from mobile laser scanning data","volume":"99","author":"Yang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.isprsjprs.2018.01.013","article-title":"An efficient global energy optimization approach for robust 3D plane segmentation of point clouds","volume":"137","author":"Dong","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.isprsjprs.2020.01.009","article-title":"Fast regularity-constrained plane fitting","volume":"161","author":"Lin","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1111\/j.1477-9730.2009.00564.x","article-title":"An improved segmentation approach for planar surfaces from unstructured 3D point clouds","volume":"25","author":"Awwad","year":"2010","journal-title":"Photogramm. Rec."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Stein, S.C., Schoeler, M., Papon, J., and Worgotter, F. (2014, January 23\u201328). Object Partitioning Using Local Convexity. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.46"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"107115","DOI":"10.1016\/j.patcog.2019.107115","article-title":"A robust statistics approach for plane detection in unorganized point clouds","volume":"100","author":"Oliveira","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Farid, R. (2015). Region-Growing Planar Segmentation for Robot Action Planning. AI 2015: Advances in Artificial Intelligence, Springer.","DOI":"10.1007\/978-3-319-26350-2_16"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2043","DOI":"10.1016\/j.patcog.2014.12.020","article-title":"Real-time detection of planar regions in unorganized point clouds","volume":"48","author":"Limberger","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.isprsjprs.2015.01.011","article-title":"Octree-based region growing for point cloud segmentation","volume":"104","author":"Vo","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1109\/34.969114","article-title":"Fast approximate energy minimization via graph cuts","volume":"23","author":"Boykov","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Armeni, I., Sener, O., Zamir, A.R., Jiang, H., Brilakis, I., Fischer, M., and Savarese, S. (2016, January 27\u201330). 3D Semantic Parsing of Large-Scale Indoor Spaces. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.170"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"103886","DOI":"10.1016\/j.autcon.2021.103886","article-title":"Indoor interior segmentation with curved surfaces via global energy optimization","volume":"131","author":"Su","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_59","first-page":"248","article-title":"Segmentation of point clouds using smoothness constraint","volume":"36","author":"Rabbani","year":"2006","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/131\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:52:01Z","timestamp":1760147521000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/131"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,26]]},"references-count":59,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010131"],"URL":"https:\/\/doi.org\/10.3390\/rs15010131","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,26]]}}}