[1] Besl P J, McKay N D. A method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 239-256(1992).

[2] Granger S, Pennec X. Multi-scale EM-ICP: a fast and robust approach for surface registration[M]. Heyden A, Sparr G, Nielsen M, et al. Computer vision-ECCV 2002. Lecture notes in computer science, 2353, 418-432(2002).

[3] Segal A, Haehnel D, Thrun S. Generalized-ICP[C](2009).

[4] Yang J L, Li H D, Campbell D et al. Go-ICP: a globally optimal solution to 3D ICP point-set registration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 2241-2254(2016).

[5] Li J, Deng Z Q, Shen N Y et al. A fully automatic surgical registration method for percutaneous abdominal puncture surgical navigation[J]. Computers in Biology and Medicine, 136, 104663(2021).

[6] Shi W, Wang L P. High-accuracy digital volume correlation-based point cloud registration for 3D reconstruction[J]. Proceedings of SPIE, 12319, 1231907(2022).

[7] Park S, Kim S, Seo H. Study on representative parameters of reverse engineering for maintenance of ballasted tracks[J]. Applied Sciences, 12, 5973(2022).

[8] Yang Z X, Ye Q, Wang X F et al. Automated registration of cross-source and multi-temporal point clouds in urban areas[J]. Chinese Journal of Lasers, 50, 1010004(2023).

[9] Li Q M, Ren J J, Pei X H et al. High-accuracy point cloud matching algorithm for weak-texture surface based on multi-modal data cooperation[J]. Acta Optica Sinica, 42, 0810001(2022).

[10] Liu L, Li Y X, Ni R S et al. Synthetic aperture radar and optical images registration based on convolutional and graph neural networks[J]. Acta Optica Sinica, 42, 2410002(2022).

[11] Liu S, Yang B, Wang Y et al. 2D/3D multimode medical image registration based on normalized cross-correlation[J]. Applied Sciences, 12, 2828(2022).

[12] Chen X Q, Guo X Q, Zhou G et al. 2D/3D medical image registration using convolutional neural network[J]. Chinese Journal of Biomedical Engineering, 39, 394-403(2020).

[13] Shabnam S, Diana P, Jennie M S et al. An efficient hybrid method for 3D to 2D medical image registration[J]. International Journal of Computer Assisted Radiology and Surgery, 17, 1313-1320(2022).

[14] Li W J, Kong D Q, Cao G G et al. Cross-modal medical image registration based on attention residual network[J]. Computer Simulation, 39, 224-229(2022).

[15] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[16] Li W J, Kong D Q, Cao G G et al. 2D-3D medical image registration based on training-inference decoupling architecture[J]. Laser & Optoelectronics Progress, 59, 1610015(2022).

[17] Cheng T Y, Gu J P, Hua L et al. Research on 3D medical image registration based on geometric algebra SURF[J]. Chinese Journal of Scientific Instrument, 43, 227-237(2022).

[18] Blendowski M, Bouteldja N, Heinrich M P. Multimodal 3D medical image registration guided by shape encoder-decoder networks[J]. International Journal of Computer Assisted Radiology and Surgery, 15, 269-276(2020).

[19] Cheng X, Zhang L, Zheng Y F. Deep similarity learning for multimodal medical images[J]. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 6, 248-252(2018).

[20] Lei C, Jia Y, Wang H et al. Comparative study on registration algorithm between point cloud image and CT image of structured light camera[J]. Chinese Medical Equipment Journal, 43, 8-13(2022).

[21] Wang Y, Sun Y B, Liu Z W et al. Dynamic graph CNN for learning on point clouds[J]. ACM Transactions on Graphics, 38, 146(2019).

[22] Lucas B D, Kanade T. An iterative image registration technique with an application to stereo vision[C], 674-679(1981).

[23] Wu Z R, Song S R, Khosla A et al. 3D ShapeNets: a deep representation for volumetric shapes[C], 1912-1920(2015).

[24] Aoki Y, Goforth H, Srivatsan R A et al. PointNetLK: robust & efficient point cloud registration using PointNet[C], 7156-7165(2020).