[1] 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, 143, 233-248(2018). http://www.sciencedirect.com/science/article/pii/S0924271618300248

[2] Zhao Z Z, Zhang Y J. Building extraction from airborne laser point cloud using NDVI constrained watershed algorithm[J]. Acta Optica Sinica, 36, 1028002(2016).

[3] Hołowko E, Wojsz J, Sitnik R et al. Color-based algorithm for automatic merging of multiview 3D point clouds[J]. ACM Journal on Computing and Cultural Heritage, 7, 16(2014). http://dl.acm.org/citation.cfm?id=2558306

[4] Li C L, Zhong F, Zhang Q et al. Accurate and fast 3D head pose estimation with noisy RGBD images[J]. Multimedia Tools and Applications, 77, 14605-14624(2018). http://www.onacademic.com/detail/journal_1000040048997010_572a.html

[5] [5] MiyakeS, TodaY, KubotaN, et al.Intensity histogram based segmentation of 3D point cloud using growing neural gas[C]∥International Conference on Intelligent Robotics and Applications, Springer International Publishing, 2016: 335-345.

[6] Bolles R C, Fischler M A. ARANSAC-based approach to model fitting and its appliAcation to finding cylinders in range data. [C]∥International Joint Conference on Artificial Intelligence, 637-643(1981).

[7] Fan T J, Medioni G, Nevatia R. Segmented descriptions of 3-D surfaces[J]. IEEE Journal on Robotics and Automation, 3, 527-538(1987). http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1087146

[8] Hu H Y, Cui H G, Dai X. Segmentation of scattered point data based on region growing method[J]. Journal of Computer Applications, 29, 2716-2718(2009).

[9] Pang S Y, Liu Y W, Zuo Z Q et al. Combination of region growing and TIN edge segmentation for extraction of geometric features on building facades[J]. Geomatics and Information Science of Wuhan University, 40, 102-106(2015).

[10] Dimitrov A, Golparvar-Fard M. Segmentation of building point cloud models including detailed architectural/structural features and MEP systems[J]. Automation in Construction, 51, 32-45(2015). http://www.sciencedirect.com/science/article/pii/S0926580514002593

[11] Nurunnabi A, Belton D, West G. Robust segmentation in laser scanning 3D point cloud data. [C]∥International Conference on Digital Image Computing Techniques and Applications (DICTA), 1-8(2012).

[12] Nurunnabi A, West G, Belton D. Outlierdetection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data[J]. Pattern Recognition, 48, 1404-1419(2015). http://dl.acm.org/citation.cfm?id=2801382

[13] Vo A V, Truong-Hong L, Laefer D F et al. Octree-based region growing for point cloud segmentation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 104, 88-100(2015). http://www.sciencedirect.com/science/article/pii/s0924271615000283

[14] Velmurugan T, Santhanam T. Computational complexity between K-means and K-medoids clustering algorithms for normal and uniform distributions of data points[J]. Journal of Computer Science, 6, 363-368(2010).

[15] Wang S, Sun H Y, Guo H C et al. Mixed manifold spectral clustering adaptive segmentation method for laser point cloud[J]. Acta Optica Sinica, 37, 1011001(2017).

[16] Xiong F G, Huo W, Han X et al. Removal method of mismatching keypoints in 3D point cloud[J]. Acta Optica Sinica, 38, 0210003(2018).

[17] Yan J Z, Qi M Y, Fang L Y et al. Forecast the distribution of urban water point by using improved DBSCAN algorithm. [C]∥International Conference on Intelligent System Design and Engineering Applications, 784-786(2013).

[18] Zhang X, Zang A, Agam G et al. Learning from synthetic models for roof style classification in point clouds. [C]∥ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 263-270(2014).

[19] Mahmoudabadi H, Olsen M J, Todorovic S. Efficient terrestrial laser scan segmentation exploiting data structure[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 119, 135-150(2016). http://www.sciencedirect.com/science/article/pii/S0924271616301083

[20] Holz D, Behnke S. Approximate triangulation and region growing for efficient segmentation and smoothing of range images[J]. Robotics and Autonomous Systems, 62, 1282-1293(2014). http://dl.acm.org/citation.cfm?id=2657397

[21] Boulch A. Guerry J, le Saux B, et al. SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks[J]. Computers & Graphics, 71, 189-198(2018).

[22] Schnabel R, Wahl R, Klein R. Efficient RANSAC forpoint-cloud shape detection[J]. Computer Graphics Forum, 26, 214-226(2007). http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8659.2007.01016.x/abstract;jsessionid=E913F6976C8605A1ABFA9CCFC3BFE73A.f01t04

[23] Papazov C, Burschka D. Anefficient RANSAC for 3D object recognition in noisy and occluded scenes. [C]∥Asian Conference on Computer Vision, 6492, 135-148(2010).

[24] Zuliani M, Kenney C S. 3: III-153[C]. Manjunath B S. The multi RANSAC algorithm, its application to detect planar homographies∥IEEE International Conference on Image Processing(2005).

[25] Tran T T, Cao V T, Laurendeau D. Extraction of cylinders and estimation of their parameters from point clouds[J]. Computers & Graphics, 46, 345-357(2015). http://www.sciencedirect.com/science/article/pii/S0097849314001150

[26] Biegelbauer G, Vincze M, Wohlkinger W. Model-based 3D object detection[J]. Machine Vision and Applications, 21, 497-516(2010). http://link.springer.com/article/10.1007/s00138-008-0178-3

[27] Zhang X M, Wan W G, Xiao L et al. Mean shift clustering segmentation and RANSAC simplification of color point cloud. [C]∥International Conference on Audio, Language and Image Processing, 837-841(2014).

[28] Kang Z Z, Li Z. Primitive fitting based on the efficient multiBaySAC algorithm[J]. PLoS ONE, 10, e0117341(2015). http://www.ncbi.nlm.nih.gov/pubmed/25781620

[29] 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, 9, 433(2017). http://adsabs.harvard.edu/abs/2017RemS....9..433L

[30] Overby J, Bodum L, Kjems E et al. Automatic 3D building reconstruction from airborne laser scanning and cadastral data using Hough transform[J]. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 34, 296-301(2004).

[31] Rabbani T, von den Heuvel F. Efficient Hough transform for automatic detection of cylinders in point clouds[J]. ISPRS Workshop on Laser Scanning, 3, 60-65(2005).

[32] Tarsha-Kurdi F, Landes F, Grussenmeyer P. Extended RANSAC algorithm for automatic detection of building roof planes from LiDAR data[J]. The Photogrammetric Journal of Finland, 21, 97-109(2008).

[33] Liu Y E, Li Z R, Hayward R et al. Classification of airborne LIDAR intensity data using statistical analysis and Hough transform with application to power line corridors. [C]∥Digital Image Computing: Techniques and Applications, 462-467(2009).

[34] Hulik R, Spanel M, Smrz P et al. Continuous plane detection in point-cloud data based on 3D Hough transform[J]. Journal of Visual Communication and Image Representation, 25, 86-97(2014). http://www.sciencedirect.com/science/article/pii/S104732031300062X

[35] Hoover A, Jean-Baptiste G, Jiang X et al. An experimental comparison of range image segmentation algorithms[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, 673-689(1996). http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=506791

[36] Várady T, Martin R R, Cox J. Reverse engineering of geometric models: an introduction[J]. Computer-Aided Design, 29, 255-268(1997). http://www.sciencedirect.com/science/article/pii/S0010448596000541