[1] Wu M L, Chien J C, Wu C T et al. An augmented reality system using improved-iterative closest point algorithm for on-patient medical image visualization[J]. Sensors, 18, 2505(2018).

[2] Chen H, Shen J. Denoising of point cloud data for computer-aided design, engineering, and manufacturing[J]. Engineering with Computers, 34, 523-541(2018).

[3] Luo N, Wang Q. Effective outlier matches pruning algorithm for rigid pairwise point cloud registration using distance disparity matrix[J]. IET Computer Vision, 12, 220-232(2018).

[4] Hubert M, Rousseeuw P J, Vanden Branden K. ROBPCA: a new approach to robust principal component analysis[J]. Technometrics, 47, 64-79(2005).

[5] Mattei E, Castrodad A. Point cloud denoising via moving RPCA[J]. Computer Graphics Forum, 36, 123-137(2017).

[6] Li R Z, Yang M, Ran Y et al. Point cloud denoising and simplification algorithm based on method library[J]. Laser & Optoelectronics Progress, 55, 011008(2018).

[7] Han X F, Yang F B, Wei H et al. Point cloud filter of skewness balance based on the guidance of multispectral data[J]. Laser & Optoelectronics Progress, 55, 042801(2018).

[8] Chetverikov D, Stepanov D, Krsek P. Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm[J]. Image and Vision Computing, 23, 299-309(2005).

[9] Marani R, Renò V, Nitti M et al. A modified iterative closest point algorithm for 3D point cloud registration[J]. Computer-Aided Civil and Infrastructure Engineering, 31, 515-534(2016).

[10] Rusu R B, Blodow N, Beetz M. Fast point feature histograms (FPFH) for 3D registration. [C]∥2009 IEEE International Conference on Robotics and Automation, May 12-17, 2009, Kobe, Japan. New York: IEEE, 3212-3217(2009).

[11] Guo Y L, Bennamoun M, Sohel F et al. A comprehensive performance evaluation of 3D local feature descriptors[J]. International Journal of Computer Vision, 116, 66-89(2016).

[12] Yang J Q, Cao Z G, Zhang Q. A fast[J]. robust local descriptor for 3D point cloud registration. Information Sciences, 346/347, 163-179(2016).

[13] 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, 24, 381-395(1981).

[14] Mellado N, Aiger D, Mitra N J. Super 4PCS fast global pointcloud registration via smart indexing[J]. Computer Graphics Forum, 33, 205-215(2014).

[15] Chen X J, Zhang G, Hua X H. Point cloud simplification based on the information entropy of normal vector angle[J]. Chinese Journal of Lasers, 42, 0814003(2015).

[16] Huang Y, Da F P, Tang L. Three-dimensional point cloud compression algorithm based on improved octree[J]. Acta Optica Sinica, 37, 1210003(2017).

[17] Jiang X T, Dai N, Cheng X S et al. Fast neighborhood search of large-scale scattered point cloud based on the binary-encoding octree[J]. Journal of Computer-Aided Design & Computer Graphics, 30, 824-832(2018).

[18] Curless B, Levoy M. A volumetric method for building complex models from range images. [C]∥Proceedings of the 23rd annual conference on Computer graphics and interactive techniques. New York: ACM, 303-312(1996).