[1] NguyenA, LeB. 3D point cloud segmentation: a survey[C]//2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM), November 12-15, 2013, Manila, Philippines. New York: IEEE, 2013: 225- 230.

[2] 欧新良, 匡小兰, 倪问尹. 三维散乱点云分割技术综述[J]. 湖南工业大学学报, 2010, 24(5): 45-49.

    Ou X L, Kuang X L, Ni W Y. Summarization on 3D scattered point cloud segmentation[J]. Journal of Hunan University of Technology, 2010, 24(5): 45-49.

[3] 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.

[4] KohJ, SukM, Bhandarkar SM. A multi-layer Kohonen's self-organizing feature map for range image segmentation[C]//IEEE International Conference on Neural Networks, March 28-April 1,1993, San Francisco, CA, USA, New York: IEEE, 1993: 4725511.

[5] Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6): 381-395.

[6] 鲁荣荣, 朱枫, 吴清潇, 等. 一种板型物体混叠场景的快速分割算法[J]. 光学学报, 2019, 39(4): 0412003.

    Lu R R, Zhu F, Wu Q X, et al. A fast segmenting method for scenes with stacked plate-shaped objects[J]. Acta Optica Sinica, 2019, 39(4): 0412003.

[7] 范小辉, 许国良, 李万林, 等. 基于深度图的三维激光雷达点云目标分割方法[J]. 中国激光, 2019, 46(7): 0710002.

    Fan X H, Xu G L, Li W L, et al. Target segmentation method for three-dimensional LiDAR point cloud based on depth image[J]. Chinese Journal of Lasers, 2019, 46(7): 0710002.

[8] KrizhevskyA, SutskeverI, HintonG. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 26th Annual Conference on Neural Information Processing Systems 2012, December 3-6, 2012, South Lake Tahoe, NV, USA.Cambridge: MIT Press, 2012: 1097- 1105.

[9] KlokovR, LempitskyV. Escape from cells: deep kd-networks for the recognition of 3D point cloud models[C]//2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2017: 863- 872.

[10] Wang P S, Liu Y, Guo Y X, et al. O-CNN: octree-based convolutional neural networks for 3D shape analysis[J]. ACM Transactions on Graphics, 2017, 36(4): 72.

[11] RieglerG, Ulusoy AO, GeigerA. OctNet: learning deep 3D representations at high resolutions[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 6620- 6629.

[12] Charles RQ, SuH, Mo KC, et al. PointNet: deep learning on point sets for 3D classification and segmentation[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 77- 85.

[13] Qi CR, YiL, SuH, et al. ( 2017-06-07)[2020-04-28]. org/abs/1706. 02413. https://arxiv.

[14] 武彦林, 全燕鸣, 郭清达. 基于局部单应性矩阵的结构光系统标定研究[J]. 科学技术与工程, 2016, 16(13): 186-189, 195.

    Wu Y L, Quan Y M, Guo Q D. Research on calibration of structured light system based on local homography matrix[J]. Science Technology and Engineering, 2016, 16(13): 186-189, 195.

[15] Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66.