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Journal of Sensors
Volume 2017, Article ID 9576850, 11 pages
https://doi.org/10.1155/2017/9576850
Review Article

Review and Comparison of High-Dynamic Range Three-Dimensional Shape Measurement Techniques

1Key Laboratory of Mechanical Equipment Manufacturing and Control Technology of Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China
2School of Physics and Mechatronics Engineering, Shaoguan University, Shaoguan 512005, China
3Guangdong Provincial Key Laboratory of Optomechatronics, Shenzhen 518057, China

Correspondence should be addressed to Jian Gao; nc.ude.tudg@naijoag

Received 8 November 2016; Revised 7 February 2017; Accepted 26 February 2017; Published 12 April 2017

Academic Editor: Armando V. Razionale

Copyright © 2017 Hui Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. S. Gorthi and P. Rastogi, “Fringe projection techniques: whither we are?” Optics and Lasers in Engineering, vol. 48, no. 2, pp. 133–140, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Zhang, Handbook of 3D Machine Vision: Optical Metrology and Imaging, CRC Press, Boca Raton, Fla, USA, 2013.
  3. Z. Zhang, “Microsoft kinect sensor and its effect,” IEEE Multimedia, vol. 19, no. 2, pp. 4–10, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. P. I. Stavroulakis and R. K. Leach, “Invited review article: review of post-process optical form metrology for industrial-grade metal additive manufactured parts,” Review of Scientific Instruments, vol. 87, no. 4, Article ID 041101, 2016. View at Publisher · View at Google Scholar · View at Scopus
  5. F. Chen, G. M. Brown, and M. Song, “Overview of three-dimensional shape measurement using optical methods,” Optical Engineering, vol. 39, no. 1, pp. 10–22, 2000. View at Publisher · View at Google Scholar · View at Scopus
  6. D. Palousek, M. Omasta, D. Koutny, J. Bednar, T. Koutecky, and F. Dokoupil, “Effect of matte coating on 3D optical measurement accuracy,” Optical Materials, vol. 40, pp. 1–9, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. F. Blais, “Review of 20 years of range sensor development,” Journal of Electronic Imaging, vol. 13, no. 1, pp. 231–243, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Sansoni, M. Trebeschi, and F. Docchio, “State-of-the-art and applications of 3D imaging sensors in industry, cultural heritage, medicine, and criminal investigation,” Sensors, vol. 9, no. 1, pp. 568–601, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Zhang and S.-T. Yau, “High dynamic range scanning technique,” Optical Engineering, vol. 48, no. 3, Article ID 033604, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. G.-H. Liu, X.-Y. Liu, and Q.-Y. Feng, “3D shape measurement of objects with high dynamic range of surface reflectivity,” Applied Optics, vol. 50, no. 23, pp. 4557–4565, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Feng, Y. Zhang, Q. Chen, C. Zuo, R. Li, and G. Shen, “General solution for high dynamic range three-dimensional shape measurement using the fringe projection technique,” Optics and Lasers in Engineering, vol. 59, pp. 56–71, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. Y. Long, S. Wang, W. Wu, and K. Liu, “Accurate identification of saturated pixels for high dynamic range measurement,” Optical Engineering, vol. 54, no. 4, Article ID 043106, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. H. Jiang, H. Zhao, and X. Li, “High dynamic range fringe acquisition: a novel 3-D scanning technique for high-reflective surfaces,” Optics and Lasers in Engineering, vol. 50, no. 10, pp. 1484–1493, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. H. Zhao, X. Liang, X. Diao, and H. Jiang, “Rapid in-situ 3D measurement of shiny object based on fast and high dynamic range digital fringe projector,” Optics and Lasers in Engineering, vol. 54, pp. 170–174, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. L. Ekstrand and S. Zhang, “Autoexposure for three-dimensional shape measurement using a digital-light-processing projector,” Optical Engineering, vol. 50, pp. 895–900, 2011. View at Google Scholar
  16. K. Zhong, Z. Li, X. Zhou, Y. Li, Y. Shi, and C. Wang, “Enhanced phase measurement profilometry for industrial 3D inspection automation,” International Journal of Advanced Manufacturing Technology, vol. 76, no. 9–12, pp. 1563–1574, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. C. Waddington and J. Kofman, “Sinusoidal fringe-pattern projection for 3-D surface measurement with variable illuminance,” in Proceedings of the International Symposium on Optomechatronic Technologies (ISOT '10), pp. 1–5, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. C. Waddington and J. Kofman, “Analysis of measurement sensitivity to illuminance and fringe-pattern gray levels for fringe-pattern projection adaptive to ambient lighting,” Optics and Lasers in Engineering, vol. 48, no. 2, pp. 251–256, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. C. Waddington and J. Kofman, “Modified sinusoidal fringe-pattern projection for variable illuminance in phase-shifting three-dimensional surface-shape metrology,” Optical Engineering, vol. 53, no. 8, Article ID 084109, 2014. View at Publisher · View at Google Scholar
  20. C. Waddington and J. Kofman, “Saturation avoidance by adaptive fringe projection in phase-shifting 3D surface-shape measurement,” in Proceedings of the International Symposium on Optomechatronic Technologies (ISOT '10), pp. 1–4, IEEE, Ontario, Canada, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. C. Waddington and J. Kofman, “Camera-independent saturation avoidance in measuring high-reflectivity-variation surfaces using pixel-wise composed images from projected patterns of different maximum gray level,” Optics Communications, vol. 333, pp. 32–37, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. D. Li and J. Kofman, “Adaptive fringe-pattern projection for image saturation avoidance in 3D surface-shape measurement,” Optics Express, vol. 22, no. 8, pp. 9887–9901, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. G. Babaie, M. Abolbashari, and F. Farahi, “Dynamics range enhancement in digital fringe projection technique,” Precision Engineering, vol. 39, pp. 243–251, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. H. Lin, J. Gao, Q. Mei, Y. He, J. Liu, and X. Wang, “Adaptive digital fringe projection technique for high dynamic range three-dimensional shape measurement,” Optics Express, vol. 24, no. 7, pp. 7703–7718, 2016. View at Publisher · View at Google Scholar · View at Scopus
  25. C. Zhang, J. Xu, N. Xi, J. Zhao, and Q. Shi, “A robust surface coding method for optically challenging objects using structured light,” IEEE Transactions on Automation Science and Engineering, vol. 11, no. 3, pp. 775–788, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. Y. Yoshinori, M. Hiroyuki, N. Osamu, and I. Tetsuo, “Shape measurement of glossy objects by range finder with polarization optical system,” Gazo Denshi Gakkai Kenkyukai Koen Yoko, vol. 200, pp. 43–50, 2003. View at Google Scholar
  27. S. Umeyama and G. Godin, “Separation of diffuse and specular components of surface reflection by use of polarization and statistical analysis of images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 5, pp. 639–647, 2004. View at Publisher · View at Google Scholar · View at Scopus
  28. T. Chen, H. P. A. Lensch, C. Fuchs, and H.-P. Seidel, “Polarization and phase-shifting for 3D scanning of translucent objects,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '07), pp. 1–8, IEEE, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  29. R. Liang, “Short wavelength and polarized phase shifting fringe projection imaging of translucent objects,” Optical Engineering, vol. 53, no. 1, Article ID 014104, 2014. View at Publisher · View at Google Scholar
  30. B. Salahieh, Z. Chen, J. J. Rodriguez, and R. Liang, “Multi-polarization fringe projection imaging for high dynamic range objects,” Optics Express, vol. 22, no. 8, pp. 10064–10071, 2014. View at Publisher · View at Google Scholar · View at Scopus
  31. S. A. Shafer, “Using color to separate reflection components,” Color Research & Application, vol. 10, no. 4, pp. 210–218, 1985. View at Publisher · View at Google Scholar · View at Scopus
  32. R. T. Tan, K. Nishino, and K. Ikeuchi, “Separating reflection components based on chromaticity and noise analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 10, pp. 1373–1379, 2004. View at Publisher · View at Google Scholar · View at Scopus
  33. T. Gevers and A. W. M. Smeulders, “Color-based object recognition,” Pattern Recognition, vol. 32, no. 3, pp. 453–464, 1999. View at Publisher · View at Google Scholar · View at Scopus
  34. R. Benveniste and C. Ünsalan, “Single stripe projection based range scanning of shiny objects under ambient light,” in Proceedings of the 24th International Symposium on Computer and Information Sciences (ISCIS '09), pp. 1–6, IEEE, September 2009. View at Publisher · View at Google Scholar · View at Scopus
  35. R. Benveniste and C. Ünsalan, “A color invariant based binary coded structured light range scanner for shiny objects,” in Proceedings of the 20th International Conference on Pattern Recognition (ICPR '10), pp. 798–801, IEEE, Istanbul, Turkey, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. R. Benveniste and C. Ünsalan, “Binary and ternary coded structured light 3D scanner for shiny objects,” in Computer and Information Sciences, E. Gelenbe, R. Lent, G. Sakellari, A. Sacan, H. Toroslu, and A. Yazici, Eds., vol. 62, pp. 241–244, Springer Netherlands, 2010. View at Google Scholar
  37. R. Benveniste and C. Ünsalan, “A color invariant for line stripe-based range scanners,” Computer Journal, vol. 54, no. 5, pp. 738–753, 2011. View at Publisher · View at Google Scholar · View at Scopus
  38. R. Benveniste and C. Ünsalan, “Nary coded structured light-based range scanners using color invariants,” Journal of Real-Time Image Processing, vol. 9, no. 2, pp. 359–377, 2014. View at Publisher · View at Google Scholar · View at Scopus
  39. R. J. Woodham, “Photometric method for determining surface orientation from multiple images,” Optical Engineering, vol. 19, pp. 1–22, 1992. View at Google Scholar
  40. A. Hertzmann and S. M. Seitz, “Example-based photometric stereo: shape reconstruction with general, varying BRDFs,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1254–1264, 2005. View at Publisher · View at Google Scholar · View at Scopus
  41. L. Shen, T. Machida, and H. Takemura, “Efficient photometric stereo technique for three-dimensional surfaces with unknown BRDF,” in Proceedings of the 5th International Conference on 3-D Digital Imaging and Modeling (3DIM '05), pp. 326–333, IEEE, Ontario, Canada, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  42. R. T. Tan and K. Ikeuchi, “Separating reflection components of textured surfaces using a single image,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 2, pp. 178–193, 2005. View at Publisher · View at Google Scholar · View at Scopus
  43. J. Wang and K. J. Dana, “Relief texture from specularities,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 446–457, 2006. View at Publisher · View at Google Scholar · View at Scopus
  44. N. G. Alldrin and D. J. Kriegman, “Toward reconstructing surfaces with arbitrary isotropic reflectance: a stratified photometric stereo approach,” in Proceedings of the IEEE 11th International Conference on Computer Vision (ICCV '07), pp. 1–8, IEEE, October 2007. View at Publisher · View at Google Scholar · View at Scopus
  45. F. E. Nicodemus, J. C. Richmond, J. J. Hsia, I. W. Ginsberg, and T. Limperis, “Geometrical considerations and nomenclature for reflectance,” in Radiometry, pp. 94–145, 1977. View at Google Scholar
  46. S. K. Nayar, K. Ikeuchi, and T. Kanade, “Determining shape and reflectance of hybrid surfaces by photometric sampling,” IEEE Transactions on Robotics and Automation, vol. 6, no. 4, pp. 418–431, 1990. View at Publisher · View at Google Scholar · View at Scopus
  47. G. J. Ward, “Measuring and modeling anisotropic reflection,” SIGGRAPH Computer Graphics, vol. 26, no. 2, pp. 265–272, 1992. View at Publisher · View at Google Scholar
  48. C. J. Li, Z. Zhang, T. Imamura, and T. Miyaki, “An efficient BRDF acquisition for glossy surface,” in Proceedings of the 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE '10), pp. V2-141–V2-145, IEEE, Chengdu, China, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  49. D. B. Goldman, B. Curless, A. Hertzmann, and S. M. Seitz, “Shape and spatially-varying BRDFs from photometric stereo,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1060–1071, 2010. View at Publisher · View at Google Scholar · View at Scopus
  50. C. Hin-Shun and J. Jiaya, “Efficient photometric stereo on glossy surfaces with wide specular lobes,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), pp. 1–8, IEEE, Anchorage, Alaska, USA, 2008.
  51. A. S. Georghiades, “Recovering 3-D shape and reflectance from a small number of photographs,” in Proceedings of the 14th Eurographics Workshop on Rendering, pp. 230–240, ACM, Leuven, Belgium, June 2003.
  52. L. Meng, L. Lu, N. Bedard, and K. Berkner, “Single-shot specular surface reconstruction with gonio-plenoptic imaging,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '15), pp. 3433–3441, IEEE, Santiago, Chile, 2015.
  53. Q. Hu, K. G. Harding, X. Du, and D. Hamilton, “Shiny parts measurement using color separation,” in Two- and Three-Dimensional Methods for Inspection and Metrology III, vol. 6000 of Proceedings of SPIE, pp. 125–132, November 2005. View at Publisher · View at Google Scholar
  54. R. Kowarschik, P. Kühmstedt, J. Gerber, W. Schreiber, and G. Notni, “Adaptive optical three-dimensional measurement with structured light,” Optical Engineering, vol. 39, no. 1, pp. 150–158, 2000. View at Publisher · View at Google Scholar · View at Scopus
  55. J. Jeong, D. Hong, and H. Cho, “Measurement of partially specular objects by controlling imaging range,” in Optomechatronic Computer-Vision Systems II, 671808, vol. 6718 of Proceedings of SPIE, October 2007. View at Publisher · View at Google Scholar
  56. S. Ri, M. Fujigaki, and Y. Morimoto, “Intensity range extension method for three-dimensional shape measurement in phase-measuring profilometry using a digital micromirror device camera,” Applied Optics, vol. 47, no. 29, pp. 5400–5407, 2008. View at Publisher · View at Google Scholar · View at Scopus
  57. Z. Song, R. Chung, and X.-T. Zhang, “An accurate and robust strip-edge-based structured light means for shiny surface micromeasurement in 3-D,” IEEE Transactions on Industrial Electronics, vol. 60, no. 3, pp. 1023–1032, 2013. View at Publisher · View at Google Scholar · View at Scopus
  58. C. Jiang, T. Bell, and S. Zhang, “High dynamic range real-time 3D shape measurement,” Optics Express, vol. 24, no. 7, pp. 7337–7346, 2016. View at Publisher · View at Google Scholar · View at Scopus