IJAT Vol.10 No.5 pp. 813-820
doi: 10.20965/ijat.2016.p0813


3D Model Reconstruction System Development Based on Laser-Vision Technology

Huu-Cuong Nguyen and Byung-Ryong Lee

University of Ulsan
Daehak-ro 93, Nam-gu, Ulsan 680-749, South Korea

Corresponding author

April 12, 2016
July 27, 2016
September 5, 2016
laser-vision technology, point cloud data, 3D model reconstruction, laser-camera calibration, 3D scanning system
We propose a three-dimensional (3D) scanning system based on laser-vision technique and rotary mechanism combination for automatic 3D model reconstruction. The proposed scanning system consists of a laser projector, camera, and turntable. For laser-camera calibration, a new and simple method is applied. The 3D point cloud data of the surface of the scanned object are fully collected by integrating the extracted laser profiles from the laser stripe images corresponding to the rotary angles of the rotary mechanism. The obscured laser profile problem is solved by an additional camera at another viewpoint. From the collected 3D point cloud data, the 3D model of the scanned object is reconstructed based on the facet-representation method. The reconstructed 3D models showed the effectiveness and applicability of the proposed 3D scanning system in many 3D model-based applications.
Cite this article as:
H. Nguyen and B. Lee, “3D Model Reconstruction System Development Based on Laser-Vision Technology,” Int. J. Automation Technol., Vol.10 No.5, pp. 813-820, 2016.
Data files:
  1. [1] Y. Qui, S. Cai, and S. Yang, “3D modeling, codec and protection in digital museum,” 2nd Workshop on Digital Media and its Application in Museum & Heritages, pp. 231-236, 2007.
  2. [2] F. Cordier, H. Seo, and N. M. Thalmann, “Made-to-measure technologies for an online clothing store,” Computer Graphics and Applications, Vol.23, pp. 38-48, 2003.
  3. [3] L. Dai, “The 3d digital technology of fashion design,” Int. Symposium on Computer Science and Society, pp. 178-180, 2011.
  4. [4] Y. Endo. M. Tada, and M. Mochimaru, “Hand modeling and motion reconstruction for individuals,” Int. J. of Automation Technology, Vol.8, pp. 376-387, 2014.
  5. [5] H. Xu, Y. Hu, Y. Chen, Z. Ma, and D. Wu, “A novel 3d surface modeling based on spatial neighbor points coupling in reverse engineering,” Int. Conf. on Computer Design and Applications, Vol.5, pp. 59-62, 2011.
  6. [6] J. Andrews, H. Jin, and C. Selquin, “Interactive inverse 3d modeling,” Computer-Aided Design and Applications, Vol.9, pp. 1-18, 2009.
  7. [7] T. Toth and J. Zivcak, “A comparison of the outputs of 3d scanners,” Procedia Engineering, Vol.69, pp. 393-401, 2014.
  8. [8] H. C. Nguyen, E. Ho, and B. R. Lee, “Surface reconstruction from multi-views data sets using stereo camera as rotatable mechanism,” Int. Conf. on Mechatronics Technology, pp. 142-145, 2013.
  9. [9] M. Levine and Y. Yu, “State-of-the-art of 3d facial reconstruction methods for face recognition based on a single 2d training image per person,” Pattern Recognition Letters, Vol.30, pp. 908-913, 2009.
  10. [10] S. Usuki, M. Uno, and K. T. Miura, “Digital shape reconstruction of a micro-sized machining tool using light-field microscopy,” Int. J. of Automation Technology, Vol.10, pp. 172-178, 2016.
  11. [11] H. C. Nguyen and B. R. Lee, “Laser-vision-based quality inspection system for small-bead laser welding,” Int. J. of Precision Eng. and Manuf., Vol.15, pp. 415-423, 2014.
  12. [12] B. Boeckmans, M. Zhang, F. Welkenhuyzen, and J. P. Kruth, “Determine of aspect ratio limitations accuracy and repeatability of a laser line scanning CMM probe,” Int. J. of Automation Technology, Vol.9, pp. 466-472, 2015.
  13. [13] Q. Wang, H. Tsuda, S. Kishimoto, Y. Tanaka, and Y. Kagawa, “Moiré techniques based on memory function of laser scanning microscope for deformation measurement at micron/submicron scales,” Int. J. of Automation Technology, Vol.9, pp. 494-501, 2015.
  14. [14] Photonfocus AG, producer of CMOS image sensors and CMOS cameras, [accessed April 1, 2015]
  15. [15] R. Y. Tang, Z. M. Zeng, and C. K. Sun, “3-step-calibration of 3D vision measurement system based-on structured light,” Int. J. of Automation Technology, Vol.8, pp. 484-489, 2014.
  16. [16] K. Enami, “Calibration using cylindrical artifacts for 3D laser measurement system,” Int. J. of Automation Technology, Vol.9, pp. 546-550, 2015.
  17. [17] V. Noila, C. Rossi, S. Savino, and S. Strano, “A method for the calibration of a 3d laser scanner,” Robotics and Computer-Integrated Manufacturing, Vol.27, pp. 479-484, 2011.
  18. [18] D. Xu, L. K. Wang, Z. G. Tu, and M. Tan, “Hybrid visual servoing control for robotics arc welding based on structured light vision,” Acta Automatica Sinica, Vol.31, pp. 596-605, 2015.
  19. [19] J. Sun, J. Zhang, Z. Liu, and G. Zhang, “A vision measurement model of laser displacement sensor and its calibration method,” Optics and Lasers in Engineering, Vol.51, pp. 1344-1352, 2013.
  20. [20] R. Usamentiaga, J. Molleda, and D. F. Garcia, “Fast and robust laser stripe extraction for 3d reconstruction in industrial environments,” Machine Vision and Applications, Vol.23, pp. 179-196, 2012.
  21. [21] Y. Jin, L. Zhang, C. Wu, and Z. Zhu, “Detection of 3d curve for shoe sole spraying based on laser triangulation measurement,” Proc. of the IEEE Int. Conf. on Automation and Logistics, pp. 865-868, 2009.
  22. [22] K. Sung, H. Lee, S. Choi, and S. Rhee, “Development of a multiline laser vision sensor for joint tracking in welding,” Welding Journal, pp. 79-85, 2009.
  23. [23] B. R Lee and H. C. Nguyen, “Development of laser-vision system for three-dimensional circle detection and radius measurement,” Optik, Vol.126, pp. 5412-5419, 2015.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 10, 2024