[1] Nguyen T T,Liu Xiuguo, Wang Hongping, et al. 3D model reconstruction based on laser scanning technique［J］. Laser & Optoelectronics Progress, 2011, 48(8): 081201.
[2] Besl P J, McKay N D. Method for registration of 3D shapes［C］. Robotics-DL tentative, International Society for Optics and Photonics, 1992: 586-606.
[3] Chen Y, Medioni G. Object modelling by registration of multiple range images［J］. Image and Vision Computing, 1992, 10(3): 145-155.
[4] Blais G, Levine M D. Registering multiview range data to create 3D computer objects［C］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8): 820-824.
[5] Wei Shengbin, Wang Shaoqing, Zhou Changhe, et al. An iterative closest point algorithm based on biunique correspondence of point clouds for 3D reconstruction［J］. Acta Optica Sinica, 2015, 35(5): 0515003.
[7] Wu Mengqi, Li Zhongwei, Zhong Kai, et al. Adaptive point cloud registration method based on geometric features and photometric features［J］. Acta Optica Sinica, 2015, 35(2): 0215002.
[8] Huang Yuan, Da Feipeng, Tao Haiqi. An automatic registration algorithm for point cloud based on feature extraction［J］. Chinese J Lasers, 2015, 42(3): 0308002.
[9] Zhang Xiao, Zhang Aiwu, Wang Zhihua. Pointcloud registration based on improved normal distribution transform algorithm［J］. Laser & Optoelectronics Progress, 2014, 51(4): 041002.
[10] Yu Kai, Jia Lei, Chen Yuqiang, et al. Deep learning: yesterday, today, and tomorrow［J］. Journal of Computer Research and Development, 2013, 50(9): 1799-1804
[11] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks［C］. Advances in Neural Information Processing Systems, 2012, 25(2): 1097-1105.
[12] Deng J, Dong W, Socher R, et al. Image net: a large-scale hierarchical image database［C］. Proceedings of 2009 IEEE on Computer Vision and Pattern Recognition, 2009: 248-255.
[13] Mikolov T, Deoras A, Povey D, et al. Strategies for training large scale neural network language models［C］. IEEE Workshop on Automatic Speech Recognition and Understanding, 2011: 196-201.
[14] Silver D, Huang A, Maddison C J, et al. Mastering the game of Go with deep neural networks and tree search［J］. Nature, 2016, 529(7587): 484-489.
[15] Le Cun Y, Kavukcuoglu K, Farabet C. Convolutional networks and applications in vision［J］. ISCAS, 2010, 14(5): 253-256.
[16] Nair V, Hinton G E. Rectifiedlinear units improve restricted Boltzmann machines［C］. Proceedings of the 27th International Conference on Machine Learning, 2010: 807-814.
[17] Kaiser M, John M, Heimann T, et al. 2D/3D registration of TEE probe from two non-orthogonal C-arm directions［C］. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2014: 283-290.
[18] Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks［C］. International Conference on Artificial Intelligence and Statistics, 2010, 9: 249-256.
[19] Shilane P, Min P, Kazhdan M, et al. The princeton shape benchmark［C］. Proceedings of 2004 IEEE on Shape Modeling Application, 2004: 167-178.
[20] Jia Y, Shelhamer E, Donahue J, et al. Caffe: Convolutional architecture for fast feature embedding［C］. Proceedings of the 22nd ACM International Conference on Multimedia, 2014: 675-678.
[21] van de Kraats E B, Penney G P, Tomazevic D, et al. Standardized evaluation methodology for 2D-3D registration［J］. IEEE Transactions on Medical Imaging, 2005, 24(9): 1177-1189.
