[1] MOLEBNY V, MCMANAMON P, STEINVALL O, et al. Laser radar: Historical prospective—from the East to the West[J]. Optical Engineering, 2016, 56(3): 031220. doi: 10.1117/1.OE.56.3.031220
[2] HAN J, SHAO L, XU D, et al. Enhanced computer vision with microsoft Kinect sensor: A review[J]. IEEE Transactions on Cyberne-tics, 2013, 43(5): 1318-1334. doi: 10.1109/TCYB.2013.2265378
[3] RUSU R B, COUSINS S. 3D is here: Point cloud library (PCL)[C]//2011 IEEE International Conference on Robotics & Automation. New York, USA: IEEE, 2011: 1-4.
[4] RANGEL J C, MORELL V, CAZORLA M, et al. Object recognition in noisy RGB-D data[C]//2015 Bioinspired Computation in Artificial Systems. Cham, Switzerland: Springer, 2015: 261-270.
[5] PARK J, KIM H, TAI Y W, et al. High quality depth map upsampling for 3D-TOF cameras[C]//2011 International Conference on Computer Vision. New York, USA: IEEE, 2011: 1623-1630.
[6] NGUYEN A, LE B. 3D point cloud segmentation: A survey[C]//2013 IEEE Conference on Robotics, Automation and Mechatronics. New York, USA: IEEE, 2013: 225-230.
[7] QI C R, SU H, MO K, et al. PointNet: Deep learning on point sets for 3D classification and segmentation[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York, USA: IEEE, 2017: 77-85.
[8] RABBANI T, HEUVEL F A, VOSSELMAN G. Segmentation of point clouds using smoothness constraint[J]. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2006, 36: 248-253.
[9] NI H, LIN X G, NING X G, et al. Edge detection and feature line tracing in 3D-point clouds by analyzing geometric properties of neighborhoods[J]. Remote Sensing, 2016, 8(9): 710. doi: 10.3390/rs8090710
[10] CHE E, OLSEN M J. Fast edge detection and segmentation of terrestrial laser scans through normal variation analysis[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, Ⅳ-2/W4: 51-57.
[11] CHE E, OLSEN M J. Multi-scan segmentation of terrestrial laser scanning data based on normal variation analysis[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 143: 233-248. doi: 10.1016/j.isprsjprs.2018.01.019
[12] CHE E, OLSEN M J. An efficient framework for mobile LiDAR trajectory reconstruction and Mo-NORVANA segmentation[J]. Remote Sensing, 2019, 11(7): 836. doi: 10.3390/rs11070836
[13] SUMMAN R, PIERCE S G, MINEO C. Novel algorithms for 3D surface point cloud boundary detection and edge reconstruction[J]. Journal of Computational Design and Engineering, 2019, 6(1): 81-91. doi: 10.1016/j.jcde.2018.02.001
[14] BENDELS G, SCHNABEL R, KLEIN R. Detecting holes in point set surfaces[J]. Journal of WSCG, 2006, 14: 89-96.
[15] ADAMS R, BISCHOF L. Seeded region growing[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(6): 641-647. doi: 10.1109/34.295913
[16] BESL P J, JAIN R C. Segmentation through variable-order surface fitting[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988, 10(2): 167-192. doi: 10.1109/34.3881
[17] NURUNNABI A, BELTON D, GEOFF W. Robust segmentation in laser scanning 3D point cloud data[C]//2012 Digital Image Computing: Techniques and Applications. New York, USA: IEEE, 2012: 1-8.
[18] KANG C L, WANG F, ZONG M M, et al. Research on improved region growing point cloud algorithm[J]. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, XLⅡ-3/W10: 153-157.
[19] TÓVÁRI D, PFEIFER N. Segmentation based robust interpolation—A new approach to laser data filtering[J]. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2005, 36-3/W19: 79-84.
[20] ZHAN Q M, LIANG Y B, XIAO Y H. Color-based segmentation of point clouds[C]//2009 ISPRS Laser Scanning Workshop. Göttingen, Germany: Copernicus Publications, 2009: 248-252.
[21] NING X, ZHANG X, WANG Y, et al. Segmentation of architecture shape information from 3D point cloud[C]//2009 Virtual Reality Continuum and its Applications in Industry. New York, USA: Association for Computing Machinery, 2009: 127-132.
[22] WU H, ZHANG X, SHI W Z, et al. An accurate and robust region-growing algorithm for plane segmentation of TLS point clouds using a multiscale tensor voting method[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(10): 4160-4168. doi: 10.1109/JSTARS.2019.2936662
[23] SCHUSTER H F. Segmentation of lidar data using the tensor voting framework[J]. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2004, 35: 1073-1078.
[24] PAIVA P V V, COGIMA C K, DEZEN-KEMPTER E, et al. Historical building point cloud segmentation combining hierarchical watershed transform and curvature analysis[J]. Pattern Recognition Letters, 2020, 135: 114-121. doi: 10.1016/j.patrec.2020.04.010
[25] SHI B Q, LIANG J, LIU Q. Adaptive simplification of point cloud using k-means clustering[J]. Computer-Aided Design, 2011, 43(8): 910-922. doi: 10.1016/j.cad.2011.04.001
[26] CHEHATA N, DAVID N, BRETAR F. LIDAR data classification using hierarchical k-means clustering[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2008, 37: 325-330.
[27] MELZER T. Non-parametric segmentation of ALS point clouds using mean shift[J]. Journal of Applied Geodesy, 2007, 1(3): 159-170.
[28] YIZONG C. Mean shift, mode seeking, and clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790-799. doi: 10.1109/34.400568
[29] ZHAO T, LI H, CAI Q, et al. Point cloud segmentation based on FPFH features[C]//2016 Chinese Intelligent Systems Conference. Cham, Switzerland: Springer, 2016: 427-436.
[30] TREVOR A J, GEDIKLI S, RUSU R B, et al. Efficient organized point cloud segmentation with connected components[C]//2013 Semantic Perception Mapping and Exploration. New York, USA: IEEE, 2013: 1-6.
[31] RUSU R B. Semantic 3D object maps for everyday manipulation in human living environments[J]. Artificial Intelligence(in German), 2010, 24(4): 345-348.
[32] FILIN S. Surface classification from airborne laser scanning data[J]. Computers & Geosciences, 2004, 30(9/10): 1033-1041.
[33] RUSU R, BLODOW N, BEETZ M. Fast point feature histograms (FPFH) for 3D registration[C]//2009 International Conference on Robotics and Automation. New York, USA: IEEE, 2009: 1848-1853.
[34] RUSU R, BRADSKI G, THIBAUX R, et al. Fast 3D recognition and pose using the viewpoint feature histogram[C]//2010 Intelligent Robots and Systems. New York, USA: IEEE, 2010: 2155-2162.
[35] CZERNIAWSKI T, SANKARAN B, NAHANGI M, et al. 6D DBSCAN-based segmentation of building point clouds for planar object classification[J]. Automation in Construction, 2018, 88: 44-58. doi: 10.1016/j.autcon.2017.12.029
[36] ESTER M, KRIEGEL H P, SANDER J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]//2nd International Conference on Knowledg Discovery and Data Mining (KDD-96). California, USA: AAAI Press, 1996: 226-231.
[37] HUANG X, CAO R, CAO Y. A density-based clustering method for the segmentation of individual buildings from filtered airborne LiDAR point clouds[J]. Journal of the Indian Society of Remote Sensing, 2018, 47(6): 907-921.
[38] PARK S, WANG S, LIM H, et al. Curved-voxel clustering for accurate segmentation of 3D LiDAR point clouds with real-time performance[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York, USA: IEEE, 2019: 6459-6464.
[39] XU Y S, TUTTAS S, HOEGNER L, et al. Geometric primitive extraction from point clouds of construction sites using VGS[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(3): 424-428. doi: 10.1109/LGRS.2017.2647816
[40] XU Y S, YE Z, HUANG R, et al. Robust segmentation and localization of structural planes from photogrammetric point clouds in construction sites[J]. Automation in Construction, 2020, 117: 103206. doi: 10.1016/j.autcon.2020.103206
[41] XIA S B, CHEN D, WANG R S, et al. Geometric primitives in LiDAR point clouds: A review[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 685-707. doi: 10.1109/JSTARS.2020.2969119
[42] BORRMANN D, ELSEBERG J, LINGEMANN K, et al. The 3D hough transform for plane detection in point clouds: A review and a new accumulator design[J]. 3D Research, 2011, 2(2): 1-13. doi: 10.1007/3DRes.02(2011)1
[43] FISCHLER M A, BOLLES R C. Random sample consensus: A pa-radigm for model fitting with apphcatlons to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6): 381-395. doi: 10.1145/358669.358692
[44] HOUGH P V C. Method and means for recognizing complex patterns: US 3069645[P]. 1962-12-18.
[45] VOSSELMAN G, DIJKMAN S. 3D building model reconstruction from point clouds and ground plans[C]//ISPRS Workshop: Land Surface Mapping and Characterization Using Laser Altimetry. Göttingen, Germany: Copernicus Publications, 2001: 37-43.
[46] WIDYANINGRUM E, GORTE B, LINDENBERGH R. Automatic building outline extraction from ALS point clouds by ordered points aided hough transform[J]. Remote Sensing, 2019, 11(14): 1727. doi: 10.3390/rs11141727
[47] SONG W, ZHANG L F, TIAN Y F, et al. 3D Hough transform algorithm for ground surface extraction from LiDAR point clouds[C]//2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). New York, USA: IEEE, 2019: 916-921.
[48] TIAN Y F, SONG W, CHEN L, et al. Fast planar detection system using a GPU-based 3D Hough transform for LiDAR point clouds[J]. Applied Sciences, 2020, 10(5): 1744. doi: 10.3390/app10051744
[49] TARSHA-KURDI F, LANDES T, GRUSSENMEYER P. Hough-transform and extended RANSAC algorithms for automatic detection of 3D building roof planes from lidar data[C]//ISPRS Workshop on Laser Scanning 2007. Göttingen, Germany: Copernicus Publications, 2007: 407-412.
[50] LI L, YANG F, ZHU H H, et al. An improved RANSAC for 3D point cloud plane segmentation based on normal distribution transformation cells[J]. Remote Sensing, 2017, 9(5): 433. doi: 10.3390/rs9050433
[51] TORR P H S, ZISSERMAN A. MLESAC: A new robust estimator with application to estimating image geometry[J]. Computer Vision and Image Understanding, 2000, 78(1): 138-156. doi: 10.1006/cviu.1999.0832
[52] ZHAO B F, HUA X H, YU K G, et al. Indoor point cloud segmentation using iterative gaussian mapping and improved model fitting[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(11): 7890-7907. doi: 10.1109/TGRS.2020.2984943
[53] WU Y, LI G Q, XIAN C H, et al. Extracting POP: Pairwise orthogonal planes from point cloud using RANSAC[J]. Computers & Graphics, 2021, 94: 43-51.
[54] PHAM T T, EICH M, REID I, et al. Geometrically consistent plane extraction for dense indoor 3D maps segmentation[C] // IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York, USA: IEEE, 2016: 4199-4204.
[55] GORTE B. Segmentation of TIN-structured surface models[C]//Proceedings Joint International Symposium on Geospatial Theory, Processing and Applications. Göttingen, Germany: Copernicus Publications, 2002, 34: 465-469.