Review of 3D Point Cloud Data Segmentation Methods

[1]Shapiro L, Stockman G. Computer Vision Prentice Hall [J]. 2001:62(3): 271-279.ShapiroLStockmanGComputer Vision Prentice Hall [J]2001623271279Search in Google Scholar

[2]Luo Xiping, Tian Jie, Zhuge infants, and the like. Summary of image segmentation [D]. 1999.LuoXipingTianJieZhuge infants, and the like. Summary of image segmentation [D]1999Search in Google Scholar

[3]B. Bhanu, S. Lee, C. Ho, and T. Henderson, Range data processing: Representation of surfaces by edges [J]. In proc.int. Pattern recognition conf. 1896: 236-238.B.BhanuS.LeeC.Ho, and T.HendersonRange data processing: Representation of surfaces by edges [J].In proc.int. Pattern recognition conf.1896236238Search in Google Scholar

[4]XY Jiang, H. Bunke, and U. Meier, Fast range image segmentation using high-level segmentation primitives [J], In 3rd IEEE Workshop on Applications of Computer Vision. 1996.XYJiangH.Bunke, and U.MeierFast range image segmentation using high-level segmentation primitives [J],In 3rd IEEE Workshop on Applications of Computer Vision1996Search in Google Scholar

[5]A. Sappa, M. Devy, Fast range image segmentation by an edge detection strategy. In 3D Digital Imaging and Modeling. 2001.A.SappaM.DevyFast range image segmentation by an edge detection strategy.In 3D Digital Imaging and Modeling2001Search in Google Scholar

[6]Wang Zongyue, Ma Hongchao, Xu Honggen, et al. Research on water body contour extraction method based on LiDAR point cloud data [J]. Wuhan University Journal of Information Science. 2010:31-26.WangZongyueMaHongchaoXuHonggenResearch on water body contour extraction method based on LiDAR point cloud data [J]Wuhan University Journal of Information Science20103126Search in Google Scholar

[7]Zucker SW. Regiorowing: childhood and adolescence [J], Computer Graphics and Image Processing. 1976, 17(1): 99-125.ZuckerSWRegiorowing: childhood and adolescence [J]Computer Graphics and Image Processing197617199125Search in Google Scholar

[8]Li Renzhong, Liu Yangyang, Yang Man, et al. 3D point cloud segmentation based on improved region growth [J]. Chinese Journal of Optics, 2018:23-26.LiRenzhongLiuYangyangYangMan3D point cloud segmentation based on improved region growth [J]Chinese Journal of Optics20182326Search in Google Scholar

[9]Gao F. 375 cases of MATLAB image processing [M]. Beijing: Posts and Telecommunications Press, 2015.GaoF.375 cases of MATLAB image processing [M]BeijingPosts and Telecommunications Press2015Search in Google Scholar

[10]Zhang L, Guo LM, He W, et al. An image segmentation algorithm based on maximal variance between-class and region growing [J]. Information and Electronic Engineering. 2005. :45-48ZhangLGuoLMHeWAn image segmentation algorithm based on maximal variance between-class and region growing [J]Information and Electronic Engineering20054548Search in Google Scholar

[11]Angelina S, Suresh LP, Veni SH K. Image segmentation based on genetic algorithm for region growth and region merging [C]. Computing, Electronics and Electrical Technologies, 2012.AngelinaSSureshLPVeniSH KImage segmentation based on genetic algorithm for region growth and region merging [C]Computing, Electronics and Electrical Technologies201210.1109/ICCEET.2012.6203833Search in Google Scholar

[12]Xiao Xiaoming, Ma Zhi, Cai Zixing, et al. An adaptive region growing algorithm for road segmentation [J]. Control Engineering, 2011, 18(3): 364.XiaoXiaomingMaZhiCaiZixingAn adaptive region growing algorithm for road segmentation [J]Control Engineering2011183364Search in Google Scholar

[13]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.BeslP JJainR CSegmentation through variable-order surface fitting [J]IEEE Transactions on pattern analysis and machine intelligence198810216719210.1109/34.3881Search in Google Scholar

[14]Chen J, Chen BQ. Architectural modeling from sparsely scanned range data [J]. International Journal of Computer Vision, 2008.ChenJChenBQArchitectural modeling from sparsely scanned range data [J]International Journal of Computer Vision2008Search in Google Scholar

[15]Vosselman G, Gorte B G H, Sithole G, et al. Recognising structure in laser scanner point clouds[J]. International archives of photogrammetry, remote sensing and spatial information sciences, 2004, 46(8): 33-38.VosselmanGGorteB G HSitholeGRecognising structure in laser scanner point clouds[J]International archives of photogrammetry, remote sensing and spatial information sciences20044683338Search in Google Scholar

[16]Koster K, Spann M. MIR: An approach to robust clustering-application to range image segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(5): 430-444.KosterKSpannMMIR: An approach to robust clustering-application to range image segmentation [J]IEEE Transactions on Pattern Analysis and Machine Intelligence200022543044410.1109/34.857001Search in Google Scholar

[17]Pu S, Vosselman G. Automatic extraction of building features from terrestrial laser scanning [J]. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2006, 36(5): 25-27.PuSVosselmanGAutomatic extraction of building features from terrestrial laser scanning [J]International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences20063652527Search in Google Scholar

[18]X. Ning, X. Zhang, Y. Wang, M. Jaeger, Segmentation of architecture shape information from 3D point cloud[J]. In VRCAI, 2009:77-81X.NingX.ZhangY.WangM.JaegerSegmentation of architecture shape information from 3D point cloud[J].In VRCAI2009778110.1145/1670252.1670280Search in Google Scholar

[19]Nguyen A, Le B. 3D point cloud segmentation: A survey[C]. 2013 6th IEEE conference on robotics, automation and mechatronics (RAM). IEEE, 2013.NguyenALeB3D point cloud segmentation: A survey[C]2013 6th IEEE conference on robotics, automation and mechatronics (RAM)IEEE2013Search in Google Scholar

[20]Yan Li, Xie Hong, Hu Xiaobin, et al. A new hybrid point cloud plane segmentation method [J]. Journal of Wuhan University (Information Science Edition), 2013:128-130.YanLiXieHongHuXiaobinA new hybrid point cloud plane segmentation method [J]Journal of Wuhan University (Information Science Edition)2013128130Search in Google Scholar

[21]Schnabel, Ruwen. Efficient RANSAC for point - cloud shape detection [J]. Computer graphics forum, 2007, 8(06): 1259-1284.SchnabelRuwenEfficient RANSAC for point - cloud shape detection [J]Computer graphics forum20078061259128410.1111/j.1467-8659.2007.01016.xSearch in Google Scholar

[22]PVC Hough (1962) Method and Means for Recognizing Complex Patterns, US Patent 3069654.PVCHough1962Method and Means for Recognizing Complex PatternsUS Patent 3069654Search in Google Scholar

[23]Tarsha-Kurdi, Fayez, Tania Landes, and Pierre Grussenmeyer. Hough-transform and extended ransac algorithms for automatic detection of 3d building roof planes from lidar data [J], 2007.Tarsha-KurdiFayezTaniaLandes, and PierreGrussenmeyerHough-transform and extended ransac algorithms for automatic detection of 3d building roof planes from lidar data [J]2007Search in Google Scholar

[24]Li L, Yang F, Zhu H, et al. An improved RANSAC for 3D point cloud plane segmentation based on normal distribution transformation cells [J]. Remote Sensing, 2017:160-163.LiLYangFZhuHAn improved RANSAC for 3D point cloud plane segmentation based on normal distribution transformation cells [J]Remote Sensing201716016310.3390/rs9050433Search in Google Scholar

[25]ZHANG Liangpei, Z Yun, C Zhenzhong, et al. Splitting and Merging Based Multi-model Fitting for Point Cloud Segmentation. Acta Geodaetica et Cartographica Sinica, 2018.ZHANGLiangpeiZYunCZhenzhongSplitting and Merging Based Multi-model Fitting for Point Cloud SegmentationActa Geodaetica et Cartographica Sinica2018Search in Google Scholar

[26]Felzenszwalb P F, Huttenlocher D P. Efficient graph-based image segmentation [J]. International journal of computer vision, 2004, 59(2): 167-181.FelzenszwalbP FHuttenlocherD PEfficient graph-based image segmentation [J]International journal of computer vision200459216718110.1023/B:VISI.0000022288.19776.77Search in Google Scholar

[27]Chen X, Golovinskiy A, Funkhouser T. A benchmark for 3D mesh segmentation [J]. Acm transactions on graphics (tog), 2009, 28(3): 1-12.ChenXGolovinskiyAFunkhouserTA benchmark for 3D mesh segmentation [J]Acm transactions on graphics (tog)200928311210.1145/1531326.1531379Search in Google Scholar

[28]URAL S, SHAN J. Min-cut Based Segmentation of Airborne LiDAR Point Clouds[C]. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B3. Melbourne, Australia: ISPRS, 2012:167-172.URALSSHANJMin-cut Based Segmentation of Airborne LiDAR Point Clouds[C]. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B3Melbourne, AustraliaISPRS201216717210.5194/isprsarchives-XXXIX-B3-167-2012Search in Google Scholar

[29]Li Minglei, Liu Shaochuang, Yang Huan, etc. A Two-level Optimized Lidar Point Cloud Scene Segmentation Method. Journal of Surveying and Mapping, 2018, 47 (2): 269-274.LiMingleiLiuShaochuangYangHuanetcA Two-level Optimized Lidar Point Cloud Scene Segmentation MethodJournal of Surveying and Mapping2018472269274Search in Google Scholar

[30]ANDONI A, INDYK P. Near-optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions[C]//Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science. Berkeley, CA, USA: IEEE, 2006:459-468.ANDONIAINDYKPNear-optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions[C]Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer ScienceBerkeley, CA, USAIEEE200645946810.1109/FOCS.2006.49Search in Google Scholar

[31]YU Yongtao, LI J, GUAN Haiyan, et al. Learning Hierarchical Features for Automated Extraction of Road Markings from 3-D Mobile LiDAR Point Clouds[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(2): 709-726.YUYongtaoLIJGUANHaiyanLearning Hierarchical Features for Automated Extraction of Road Markings from 3-D Mobile LiDAR Point Clouds[J]IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing20158270972610.1109/JSTARS.2014.2347276Search in Google Scholar

[32]PANG Guan, NEUMANN U. Training-based Object Recognition in Cluttered 3D Point Clouds[C]. Proceedings of 2013 International Conference on 3D Vision-3DV 2013. Seattle, WA, USA: IEEE, 2013:87-94.PANGGuanNEUMANNUTraining-based Object Recognition in Cluttered 3D Point Clouds[C]Proceedings of 2013 International Conference on 3D Vision-3DV 2013Seattle, WA, USAIEEE2013879410.1109/3DV.2013.20Search in Google Scholar

[33]Yang Bisheng, Mei Baoyan. Research on 3D City Model Visualization [D]. 2000.YangBishengMeiBaoyanResearch on 3D City Model Visualization [D]2000Search in Google Scholar

[34]Qi C R, Su H, Mo K, et al. Pointnet: Deep learning on point sets for 3d classification and segmentation[C]. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 652-660.QiC RSuHMoKPointnet: Deep learning on point sets for 3d classification and segmentation[C]Proceedings of the IEEE conference on computer vision and pattern recognition2017652660Search in Google Scholar

[35]Zhao Zhongyang, Cheng Yinglei, Shi Xiaosong, et al. LiDAR point cloud feature classification method based on multi-scale features and PointNet [J]. Progress in Laser and Optoelectronics, 2019, 56 (5): 052804.ZhaoZhongyangChengYingleiShiXiaosongLiDAR point cloud feature classification method based on multi-scale features and PointNet [J]Progress in Laser and Optoelectronics201956505280410.3788/LOP56.052804Search in Google Scholar

[36]Niu Chengeng, Liu Yujie, Li Zongmin, et al. Method of 3D target recognition and model segmentation based on point cloud data [J]. Journal of Graphics, 2019, 40 (2): 274-281.NiuChengengLiuYujieLiZongminMethod of 3D target recognition and model segmentation based on point cloud data [J]Journal of Graphics2019402274281Search in Google Scholar