Opto-Electronics Review

, Volume 21, Issue 1, pp 23–38 | Cite as

Structured light camera calibration

  • P. Garbat
  • W. SkarbekEmail author
  • M. Tomaszewski
Special Issue


Structured light camera which is being designed with the joined effort of Institute of Radioelectronics and Institute of Optoelectronics (both being large units of the Warsaw University of Technology within the Faculty of Electronics and Information Technology) combines various hardware and software contemporary technologies. In hardware it is integration of a high speed stripe projector and a stripe camera together with a standard high definition video camera. In software it is supported by sophisticated calibration techniques which enable development of advanced application such as real time 3D viewer of moving objects with the free viewpoint or 3D modeller for still objects.


3D viewer 3D modeller structured light method camera calibration 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    S.J Koppal, S. Yamazaki, S.G. Narasimhan, “Exploiting dlp illumination dithering for reconstruction and photography of high-speed scenes”, Int. J. Comput. Vision 96, 125–144 (2012).MathSciNetCrossRefGoogle Scholar
  2. 2.
    V.C. Paquit, K.W. Tobin, J.R. Price, and F. Mèriaudeau, “3D and multispectral imaging for subcutaneous veins detection”, Opt. Express 17, 11360–11365 (2009).ADSCrossRefGoogle Scholar
  3. 3.
    W. Gao, L. Wang, and Z.Y. Hu, “A flexible method for structured light system calibration”, Opt. Eng. 47, 083602 (2008).ADSCrossRefGoogle Scholar
  4. 4.
    Q.A. Li, M. Biswas, M.R. Pickering, M.R. Frater, “Accurate depth estimation using structured light and passive stereo disparity estimation”, IEEE Int. Conf. Image Process., Brussels, pp. 969–972 (2011).Google Scholar
  5. 5.
  6. 6.
    G. Wiora, “High resolution measurement of phase-shift amplitude and numeric object phase calculation”, Proc. SPIE 4117, 289–299 (2000).ADSCrossRefGoogle Scholar
  7. 7.
    Microsoft Kinect. Available online: (accessed on).
  8. 8.
    K. Khoshelham, S.O. Elberink, “Accuracy and resolution of kinect depth data for indoor mapping applications”, Sensors 12, 1437–1454 (2012).CrossRefGoogle Scholar
  9. 9.
    J. Ghring, “Dense 3-d surface acquisition by structured light using off-the-shelf components”, Proc. SPIE Videometrics and Optical Methods for 3D Shape Measurement 4309, 220–231 (2001).ADSCrossRefGoogle Scholar
  10. 10.
    E. Horn and N. Kiryati, “Toward optimal structured light patterns”, Image Vision Comput. 17, 87–97 (1999).CrossRefGoogle Scholar
  11. 11.
    E. Trucco, R.B. Fisher, A.W. Fitzgibbon, and D.K. Naidu, “Calibration, data consistency and model acquisition with laser stripers”, Int. J. Computer Integrated Manufacturing 11, 293–310 (1998).CrossRefGoogle Scholar
  12. 12.
    R.J. Valkenburg and A.M. McIvor, “Accurate 3d measurement using a structured light system”, Image Vision Comput. 16, 99–110 (1998).CrossRefGoogle Scholar
  13. 13.
    J.L. Posdamer and M.D. Altschuler, “Surface measurement by space-encoded projected beam systems”, Comput. Graph. Image Process. 18, 1–17 (1982).CrossRefGoogle Scholar
  14. 14.
    Z.J. Geng, “Rainbow 3-dimensional camera: New concept of high-speed 3-dimensional vision systems”, Opt. Eng. 35, 376–383 (1996).ADSCrossRefGoogle Scholar
  15. 15.
    C. Wust and D.W. Capson, “Surface profile measurement using colour fringe projection”, Mach. Vision Appl. 4, 193–203 (1991).CrossRefGoogle Scholar
  16. 16.
    T. Pajdla, “Bcrf — binary-coded illumination range finder reimplementation”, Technical report KUL/ESAT/MI2/9502, Katholieke Universiteit Leuven, ESAT, Leuven, 1995.Google Scholar
  17. 17.
    P. Lavoie, D. Ionescu, and E. Petriu, “A high precision 3D object reconstruction method using a colour coded grid and nurbs”, Proc. Int. Conf. Image Analysis and Processing, pp. 370–375, Venice, 1999.Google Scholar
  18. 18.
    J. Tajima and M. Iwakawa, “3-D data acquisition by rainbow range finder”, Proc. IEEE Int. Conf. Pattern Recogn., pp. 309–313, Atlantic City, 1990.Google Scholar
  19. 19.
    D. Bergmann, “New approach for automatic surface reconstruction with coded light”, Proc. SPIE Remote Sensing and Reconstruction for Three-Dimensional Objects and Scenes, Vol. 2572, pp. 2–9, San Diego, 1995.ADSCrossRefGoogle Scholar
  20. 20.
    M. Ito and A. Ishii, “A three-level checkerboard pattern (TCP) projection method for curved surface measurement”, Pattern Recogn. 28, 27–40 (1995).CrossRefGoogle Scholar
  21. 21.
    S. Kiyasu, H. Hoshino, K. Yano, and S. Fujimura, “Measurement of the 3-D shape of specular polyhedrons using an m-array coded light source”, IEEE T. Instrumentation and Measurement 44, 775–778 (1995).CrossRefGoogle Scholar
  22. 22.
    S. Inokuchi, K. Sato, and F. Matsuda, “Range-imaging for 3-D object recognition”, Int. Conf. Pattern Recogn., pp. 806–808, Montreal, 1984.Google Scholar
  23. 23.
    W. Krattenthaler, K.J. Mayer, and H.P. Duwe, “3D-surface measurement with coded light approach”, Proc. 17th Meeting of the Austrian Association for Pattern Recognition on Image Analysis and Synthesis, Vol. 12, pp. 103–114, 1995.Google Scholar
  24. 24.
    K. Sato, “Range imaging based on moving pattern light and spatio-temporal matched filter”, IEEE Int. Conf. Image Process. 1, pp. 33–36, Lausanne, 1996.Google Scholar
  25. 25.
    T. Monks and J. Carter, “Improved stripe matching for colour encoded structured light”, 5th Int. Conf. Computer Anal. Images and Patterns, pp. 476–485, Budapest, 1993.Google Scholar
  26. 26.
    E.M. Petriu, Z. Sakr, S. H. J. W., and A. Moica, “Object recognition using pseudo-random colour encoded structured light”, Proc. IEEE 17th IEEE Instrumentation and Measurement Technology Conference, Vol. 3, pp.1237–1241, Baltimore, 2000.Google Scholar
  27. 27.
    H. Morita, K. Yajima, and S. Sakata, “Reconstruction of surfaces of 3-d objects by m-array pattern projection method”, in IEEE Int. Conf. Comput. Vision, pp. 468–473, Tampa, 1988.Google Scholar
  28. 28.
    J. Salvi, J. Pages, and J. Batlle, “Pattern codification strategies in structured light systems”, Pattern Recogn. 37, 827–849 (2004).zbMATHCrossRefGoogle Scholar
  29. 29.
    C. Chen, Y. Hung, C. Chiang, and J. Wu, “Range data acquisition using colour structured lighting and stereo vision”, Image Vision Comput. 15, 445–456 (1997).CrossRefGoogle Scholar
  30. 30.
    J. Salvi, J. Batlle, and E. Mouaddib, “A robust-coded pattern projection for dynamic 3d scene measurement”, Int. J. Pattern Recogn. Lett. 19, 1055–1065 (1998).CrossRefGoogle Scholar
  31. 31.
    P. Griffin, L. Narasimhan, and S. Yee, “Generation of uniquely encoded light patterns for range data acquisition”, Pattern Recogn. 25, 609–616 (1992).CrossRefGoogle Scholar
  32. 32.
    I. Ishii, K. Yamamoto, K. Doi, and T. Tsuji, “High-speed 3D image acquisition using coded structured light projection”, IEEE/RSJ Int. Conf. Intelligent Robots and Systems, pp. 925–930, San Diego, 2007.Google Scholar
  33. 33.
    L. Zhang, B. Curless, and S.M. Seitz, “Rapid shape acquisition using colour structured light and multi-pass dynamic programming”, Int. Symp. on 3D Data Processing Visualization and Transmission, Padova, 2002.Google Scholar
  34. 34.
    P. Fechteler and P. Eisert, “Adaptive colour classification for structured light systems”, IET J. Comput. Vision 3, 49–59, 2009.CrossRefGoogle Scholar
  35. 35.
    J. Pages, J. Salvi, C. Collewet, and J. Forest, “Optimised De Bruijn patterns for one-shot shape acquisition”, Image Vision Comput. 23, 707–720 (2005).CrossRefGoogle Scholar
  36. 36.
    D. Caspi, N. Kiryati, and J. Shamir, “Range imaging with adaptive colour structured light”, Pattern Anal. Machine Intel. 20, 470–480 (1998).CrossRefGoogle Scholar
  37. 37.
    K.L. Boyer and A.C. Kak, “Colour-encoded structured light for rapid active ranging”, IEEE T. Pattern Analy. and Machine Intel. 9, 14–28 (1987).CrossRefGoogle Scholar
  38. 38.
    H. Fredricksen, “A survey of full length nonlinear shift register cycle algorithms”, Society of Industrial and Applied Mathematics Review 24, 195–221 (1982).MathSciNetzbMATHGoogle Scholar
  39. 39.
    P. Vuylsteke and A. Oosterlinck, “Range image acquisition with a single binary-encoded light pattern”, IEEE T. Pattern Anal. and Machine Intel. 12, 148–163 (1990).CrossRefGoogle Scholar
  40. 40.
    O. Hall-Holt and S. Rusinkiewicz, “Stripe boundary codes for real-time structured-light range scanning of moving objects”, in 8th IEEE Int. Conf. Comput. Vision, pp. 359–366, Vancouver, 2001.Google Scholar
  41. 41.
    Z. Zhang, “A Flexible New Technique for Camera Calibration”, IEEE T. Pattern Anal. and Machine Intel. 22, 1330–1334 (2000).CrossRefGoogle Scholar
  42. 42.
    C. Harris and M. Stephens, “A combined corner and edge detector”, Proc. 4th Alvey Vision Conf., Manchester, 1998.Google Scholar
  43. 43.
    O. Faugeras, Three-Dimensional Computer Vision, edited by MIT Press, Cambridge, 1993.Google Scholar
  44. 44.
    M. Pharr and G. Humphreys, Physically Based Rendering, edited by Morgan-Kauffman, Burlington, 2004.Google Scholar
  45. 45.
    G. Sansoni, S. Lazzari, S. Peli, and F. Docchio, “3d imager for dimensional gauging of industrial workpieces: state of the art of the development of a robust and versatile system”, Int. Conf. Recent Advances in 3-D Digital Imaging and Modeling, pp. 19–26, Ottawa, 1997.Google Scholar

Copyright information

© Versita Warsaw and Springer-Verlag Wien 2013

Authors and Affiliations

  1. 1.Faculty of Electronics and Information TechnologyWarsaw University of TechnologyWarsawPoland

Personalised recommendations