[1] 王呈, 刘涛, 穆轩. 航空发动机叶片气膜孔测量技术研究[J]. 计测技术, 32, 27-30(2012).

     WANG CH, LIU T, MU X et al. Research on aero engine blade film hole measuring technology[J]. Metrology & Measurement Technology, 32, 27-30(2012).

[2] 冯亚洲, 任军学, 刘战锋. 复杂薄壁构件自适应加工工艺几何模型重构[J]. 航空学报, 42, 524820(2021).

     FENG Y Z, REN J X, LIU Z F et al. Model construction of complex thin-wall structure parts for adaptive machining[J]. Acta Aeronautica et Astronautica Sinica, 42, 524820(2021).

[3] 董一巍, 吴宗璞, 李效基. 叶片气膜孔加工与测量技术的现状及发展趋势[J]. 航空制造技术, 61, 16-25(2018).

     DONG Y W, WU Z P, LI X J et al. Current situation and development trend of processing and measurement technology for turbine blade film cooling hole[J]. Aeronautical Manufacturing Technology, 61, 16-25(2018).

[4] CHEN X R, CAI P, SHI W K. The latest development of optical non-contact 3D profile measurement[J]. Opt. Precision Eng., 10, 528-532(2002).

     陈晓荣, 蔡萍, 施文康. 光学非接触三维形貌测量技术新进展[J]. 光学 精密工程, 10, 528-532(2002).

[5] LENG H W, XU CH G, XIAO D G et al. A method for measuring complicated deep-hole profile using line-structured-light sensor[J]. Transactions of Beijing Institute of Technology, 33, 139-143(2013).

     冷惠文, 徐春广, 肖定国. 基于线结构光的复杂深孔内轮廓三维测量方法[J]. 北京理工大学学报, 33, 139-143(2013).

[6] KARASZEWSKI M, SITNIK R, BUNSCH E. On-line， collision-free positioning of a scanner during fully automated three-dimensional measurement of cultural heritage objects[J]. Robotics and Autonomous Systems, 60, 1205-1219(2012).

[7] SITNIK R, KARASZEWSKI M, ZALUSKI W et al. Automated full-3D shape measurement of cultural heritage objects[C](2009).

[8] YANG S R. Automatic Measurement Method and Technology of Complex Curved Surface of Large Components[D](2017).

     杨守瑞. 大型构件复杂曲面自动化测量方法与技术[D](2017).

[9] 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.

[10] AIGER D, MITRA N J, COHEN-OR D. 4-points congruent sets for robust pairwise surface registration[J]. ACM Transactions on Graphics, 27, 1-10(2008).

[11] MELLADO N, AIGER D, MITRA N J. Super 4PCS fast global pointcloud registration via smart indexing[J]. Computer Graphics Forum, 33, 205-215(2014).

[12] CHARLES R Q, HAO S, MO K C et al. PointNet： deep learning on point sets for 3D classification and segmentation[C], 21, 77-85(2017).

[13] SARODE V, LI X Q, GOFORTH H et al. PCRNet： point cloud registration network using PointNet encoding[webpage], 1908-07906. https：//arxiv.org/abs/1908.07906v2

[14] SARODE V, LI X Q, GOFORTH H et al. One framework to register them all： PointNet encoding for point cloud alignment[webpage], 1912-05766. https：//arxiv.org/abs/1912.05766v1

[15] RUSU R B, BLODOW N, BEETZ M. Fast point feature histograms （FPFH） for 3D registration[C], 1-8(17).

[16] WANG Y, SOLOMON J. Deep closest point： learning representations for point cloud registration[C](2019).

[17] VASWANI A, SHAZEER N, PARMAR N et al. Attention is all you need[C], 5998-6008(2017).

[18] WU Y, YAO Q L, FAN X L et al. PANet： a point-attention based multi-scale feature fusion network for point cloud registration[J]. IEEE Transactions on Instrumentation Measurement, 72, 3271757(2023).

[19] XU H, LIU S C, WANG G F et al. OMNet： learning overlapping mask for partial-to-partial point cloud registration[C], 70, 1-12(2021).

[20] XU H, YE N J, LIU G H et al. FINet： dual branches feature interaction for partial-to-partial point cloud registration[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 2848-2856(2022).