激光与光电子学进展, 2017, 54 (3): 031001, 网络出版: 2017-03-08  

基于卷积神经网络的点云配准方法 下载: 2240次

Point Cloud Registration Based on Convolutional Neural Network
作者单位
1 北京航空航天大学电子信息工程学院, 北京 100191
2 河北远东通信系统工程有限公司, 河北 石家庄 050200
引用该论文

舒程珣, 何云涛, 孙庆科. 基于卷积神经网络的点云配准方法[J]. 激光与光电子学进展, 2017, 54(3): 031001.

Shu Chengxun, He Yuntao, Sun Qingke. Point Cloud Registration Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2017, 54(3): 031001.

参考文献

[1] Nguyen T T,刘修国, 王红平,等. 基于激光扫描技术的三维模型重建[J]. 激光与光电子学进展, 2011, 48(8): 081201.

    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] 韦盛斌, 王少卿, 周常河, 等. 用于三维重建的点云单应性迭代最近点配准算法[J]. 光学学报, 2015, 35(5): 0515003.

    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.

[6] 左 超, 鲁 敏, 谭志国, 等. 一种新的点云拼接算法[J]. 中国激光, 2012, 39(12): 1214004.

    Zuo Chao, Lu Min, Tan Zhiguo, et al. A novel algorithm for registration of point clouds[J]. Chinese J Lasers, 2012, 39(12): 1214004.

[7] 伍梦琦, 李中伟, 钟 凯, 等. 基于几何特征和图像特征的点云自适应拼接方法[J]. 光学学报, 2015, 35(2): 0215002.

    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] 黄 源, 达飞鹏, 陶海跻. 一种基于特征提取的点云自动配准算法[J]. 中国激光, 2015, 42(3): 0308002.

    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] 张 晓, 张爱武, 王致华. 基于改进正态分布变换算法的点云配准[J]. 激光与光电子学进展, 2014, 51(4): 041002.

    Zhang Xiao, Zhang Aiwu, Wang Zhihua. Pointcloud registration based on improved normal distribution transform algorithm[J]. Laser & Optoelectronics Progress, 2014, 51(4): 041002.

[10] 余 凯, 贾 磊, 陈雨强, 等. 深度学习的昨天、今天和明天[J]. 计算机研究与发展, 2013, 50(9): 1799-1804.

    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.

舒程珣, 何云涛, 孙庆科. 基于卷积神经网络的点云配准方法[J]. 激光与光电子学进展, 2017, 54(3): 031001. Shu Chengxun, He Yuntao, Sun Qingke. Point Cloud Registration Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2017, 54(3): 031001.

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