Robust Dense Depth Acquisition Using 2-D De Bruijn Structured Light

  • Zhiliang Xu
  • Lizhuang Ma
  • Wuzheng Tan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4740)


We present a new dense depth acquisition method using 2-D De Bruijn structured light, which is robust to various textures and is able to reconstruct dense depth maps of moving and deforming objects. A 2-D binary De Bruijn pattern is emitted to the target object by an off-the-shelf projector. Fast dynamic programming based stereo matching is performed on images taken from two different views. The depth is obtained by robust least square triangulation. The advantages include that we do not need to take image sequences with different illumination patterns and do not assume that the surface for reconstruction has uniform texture. Experimental results show that shapes can be efficiently obtained in good quality by the proposed approach. We believe that our approach is a good choice in applications of acquiring depth maps for moving scenes with inexpensive equipments.


Depth acquisition range sensing 3-D model reconstruction De Bruijn sequence 


  1. 1.
    Scharstein, D., Szeliski, R.: A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. Int. J. Computer Vision 47(1-3), 7–42 (2002)CrossRefzbMATHGoogle Scholar
  2. 2.
    Brown, M.Z., Burschka, D., Hager, G.D.: Advances in Computational Stereo. IEEE Trans. Pattern Analysis and Machine Intelligence 25(8), 993–1008 (2003)CrossRefGoogle Scholar
  3. 3.
    Salvi, J., Pagès, J., Batlle, J.: Pattern Codification Strategies in Structured Light Systems. Pattern Recognition 37(4), 827–849 (2004)CrossRefzbMATHGoogle Scholar
  4. 4.
    Rusinkiewicz, S., Hall-Holt, O., Levoy, M.: Real-Time 3D Model Acquisition. In: SIGGRAPH 2002 Conference Proceedings, pp. 438–446 (2002)Google Scholar
  5. 5.
    Scharstein, D., Szeliski, R.: High-Accuracy Stereo Depth Maps Using Structured Light. In: IEEE computer society conference on computer vision and pattern recognition, vol. 1, pp. 195–202 (2003)Google Scholar
  6. 6.
    Lavoie, P., Ionescu, D., Petriu, E.M.: 3-D Object Model Recovery from 2-D Images Using Structured Light. IEEE Trans. Instrum. Meas. 53(2), 437–443 (2004)CrossRefGoogle Scholar
  7. 7.
    Pagès, J., Salvi, J., Forest, J.: A New Optimised De Bruijn Coding Strategy for Structured Light Patterns. In: 17th Int. Conf. Pattern Recognition, vol. 4, pp. 284–287 (2004)Google Scholar
  8. 8.
    Morano, R.A., Ozturk, C., Conn, R., Dubin, S., Zietz, S., Nissanov, J.: Structured Light Using Pseudorandom Codes. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 322–327 (1998)CrossRefGoogle Scholar
  9. 9.
    Tsai, R.Y.: A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the-Shelf TV Cameras and Lenses. IEEE Trans. Robotics and Automation 3(4), 323–344Google Scholar
  10. 10.
    Zhang, Z.: A Flexible New Technique for Camera Calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)CrossRefGoogle Scholar
  11. 11.
    Heikkilä, J., Olli Silvén, O.: A Four-step Camera Calibration Procedure with Implicit Image Correction. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 1106–1112. IEEE Computer Society Press, Los Alamitos (1997)CrossRefGoogle Scholar
  12. 12.
    Zeller, C., Faugeras, O.: Camera Self-calibration from Video Sequences: The Kruppa Equations Revisited. Research Report 2793, INRIA (February 1996)Google Scholar
  13. 13.
    Loop, C., Zhang, Z.: Computing Rectifying Homographies for Stereo Vision. In: Proc. IEEE Computer Science Conference on Computer Vision and Pattern Recognition, pp. 125–131 (1999)Google Scholar
  14. 14.
    Lewis, J.P.: Fast Normalized Cross-Correlation. In: Proceedings of Vision Interface (VI 1995), pp. 120–123 (1995)Google Scholar
  15. 15.
    Cox, I.J., Hingorani, S.L., Rao, S.B., Maggs, B.M.: A Maximum Likelihood Stereo Algorithm. Computer Vision and Image Understanding 63, 542–567 (1996)CrossRefGoogle Scholar
  16. 16.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C: The Art of Scientific Computing, 2nd edn., pp. 59–70. Cambridge University Press, Cambridge (1992)zbMATHGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2007

Authors and Affiliations

  • Zhiliang Xu
    • 1
  • Lizhuang Ma
    • 1
  • Wuzheng Tan
    • 1
  1. 1.Department of Computer Science & Engineering, Shanghai Jiaotong University, No. 800, Dongchuan Rd., Shanghai 200240P.R. China

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