
A Dual-Platform Laser Scanner for 3D Reconstruction of Dental Pieces
Shuming Yang, Xinyu Shi, Guofeng Zhang, Changshuo Lv
Engineering ›› 2018, Vol. 4 ›› Issue (6) : 796-805.
A Dual-Platform Laser Scanner for 3D Reconstruction of Dental Pieces
This paper presents a dual-platform scanner for dental reconstruction based on a three-dimensional (3D) laser-scanning method. The scanner combines translation and rotation platforms to perform a holistic scanning. A hybrid calibration method for laser scanning is proposed to improve convenience and precision. This method includes an integrative method for data collection and a hybrid algorithm for data processing. The integrative method conveniently collects a substantial number of calibrating points with a stepped gauge and a pattern for both the translation and rotation scans. The hybrid algorithm, which consists of a basic model and a compensation network, achieves strong stability with a small degree of errors. The experiments verified the hybrid calibration method and the scanner application for the measurement of dental pieces. Two typical dental pieces were measured, and the experimental results demonstrated the validity of the measurement that was performed using the dual-platform scanner. This method is effective for the 3D reconstruction of dental pieces, as well as that of objects with irregular shapes in engineering fields.
Laser scanning / Hybrid calibration / Neural network / Dental pieces
[1] |
Welk A., Rosin M., Seyer D., Splieth C., Siemer M., Meyer G.. German dental faculty attitudes towards computer-assisted learning and their correlation with personal and professional profiles. Eur J Dent Educ. 2005; 9(3): 123-130.
|
[2] |
Munera N., Lora G.J., Garcia-Sucerquia J.. Evaluation of fringe projection and laser scanning for 3D reconstruction of dental pieces. Dyna. 2012; 79(171): 65-73.
|
[3] |
Geng J.. Structured-light 3D surface imaging: a tutorial. Adv Opt Photonics. 2011; 3(2): 128-160.
|
[4] |
Zhou W., Guo H., Li Q., Hong T.. Fine deformation monitoring of ancient building based on terrestrial laser scanning technologies. IOP Conf Ser Earth Environ Sci. 2014; 17: 012166.
|
[5] |
Andersen U.V., Pedersen D.B., Hansen H.N., Nielsen J.S.. In-process 3D geometry reconstruction of objects produced by direct light projection. Int J Adv Manuf Technol. 2013; 68(1–4): 565-573.
|
[6] |
Choi S., Kim P., Boutilier R., Kim M.Y., Lee Y.J., Lee H.. Development of a high speed laser scanning confocal microscope with an acquisition rate up to 200 frames per second. Opt Express. 2013; 21(20): 23611-23618.
|
[7] |
Dewar R.. Self-generated targets for spatial calibration of structured light optical sectioning sensors with respect to an external coordinate system.
|
[8] |
Duan F.J., Liu F.M., Ye S.H.. A new accurate method for the calibration of line structured light sensor. Chin J Sci Instrum. 2000; 21: 108-113. Chinese
|
[9] |
Huynh D.Q., Owens R.A., Hartmann P.E.. Calibration a structured light stripe system: a novel approach. Int J Comput Vis. 1999; 33(1): 73-86.
|
[10] |
Zhou F., Zhang G.. Complete calibration of a structured light stripe vision sensor through planar target of unknown orientations. Image Vis Comput. 2005; 23(1): 59-67.
|
[11] |
Sun Q., Hou Y., Tan Q., Li G.. A flexible calibration method using the planar target with a square pattern for line structured light vision system. PLoS One. 2014; 9(9): e106911.
|
[12] |
Xie Z., Wang X., Chi S.. Simultaneous calibration of the intrinsic and extrinsic parameters of structured-light sensors. Opt Lasers Eng. 2014; 58: 9-18.
|
[13] |
Li J., Chen M., Jin X., Chen Y., Dai Z., Ou Z.,
|
[14] |
Li P., Zhang W., Xiong X.. A fast approach for calibrating 3D coordinate measuring system rotation axis based on line-structure light. Microcomput Appl. 2015; 34: 73-75. Chinese
|
[15] |
Wu Q., Li J., Su X., Hui B.. An approach for calibration rotor position of three-dimensional measurement system for line-structure light. Chin J Lasers. 2008; 35(8): 1224-1227. Chinese
|
[16] |
Chang M., Tai W.C.. 360-deg profile noncontact measurement using a neural network. Opt Eng. 1995; 34(12): 3572-3577.
|
[17] |
Dipanda A., Woo S., Marzani F., Bilbault J.M.. 3D shape reconstruction in an active stereo vision system using genetic algorithms. Patt Recog. 2003; 36(9): 2143-2159.
|
[18] |
Zhao Y., Ren H., Xu K., Hu J.. Method for calibrating intrinsic camera parameters using orthogonal vanishing points. Opt Eng. 2016; 55(8): 084106.
|
[19] |
Tsai R.Y.. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J Robot Autom. 1987; 3(4): 323-344.
|
[20] |
Zhang Z.. A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell. 2000; 22(11): 1330-1334.
|
[21] |
Li X.W., Cho S.J., Kim S.T.. Combined use of BP neural network and computational integral imaging reconstruction for optical multiple-image security. Opt Commun. 2014; 315(6): 147-158.
|
[22] |
Wei P., Cheng C., Liu T.. A photonic transducer-based optical current sensor using back-propagation neural network. IEEE Photonics Technol Lett. 2016; 28(14): 1513-1516.
|
[23] |
Zhang Y., Liu W., Li X., Yang F., Gao P., Jia Z.. Accuracy improvement in laser stripe extraction for large-scale triangulation scanning measurement system. Opt Eng. 2015; 54(10): 105108.
|
[24] |
Watson D.F.. Computing the n-dimensional Delaunay tessellation with applications to Voronoi polytopes. Comput J. 1981; 24(2): 167-172.
|
The authors are grateful for support from the National Science Fund for Excellent Young Scholars (51722509), the National Natural Science Foundation of China (51575440), the National Key R&D Program of China (2017YFB1104700), and the Shaanxi Science and Technology Project (2016GY-011).
Shuming Yang, Xinyu Shi, Guofeng Zhang, and Changshuo Lv declare that they have no conflict of interest or financial conflicts to disclose.
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