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

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Part of the book series:Lecture Notes in Computer Science ((LNISA,volume 4740))

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Abstract

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.

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References

  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)

    Article MATH  Google Scholar 

  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)

    Article  Google Scholar 

  3. Salvi, J., Pagès, J., Batlle, J.: Pattern Codification Strategies in Structured Light Systems. Pattern Recognition 37(4), 827–849 (2004)

    Article MATH  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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), 323–344

    Google Scholar 

  10. Zhang, Z.: A Flexible New Technique for Camera Calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  12. Zeller, C., Faugeras, O.: Camera Self-calibration from Video Sequences: The Kruppa Equations Revisited. Research Report 2793, INRIA (February 1996)

    Google Scholar 

  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. Lewis, J.P.: Fast Normalized Cross-Correlation. In: Proceedings of Vision Interface (VI 1995), pp. 120–123 (1995)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Shanghai Jiaotong University, No. 800, Dongchuan Rd., Shanghai 200240, P.R. China

    Zhiliang Xu, Lizhuang Ma & Wuzheng Tan

Authors
  1. Zhiliang Xu

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  2. Lizhuang Ma

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  3. Wuzheng Tan

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Editor information

Lizhuang Ma Matthias Rauterberg Ryohei Nakatsu

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© 2007 IFIP International Federation for Information Processing

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Xu, Z., Ma, L., Tan, W. (2007). Robust Dense Depth Acquisition Using 2-D De Bruijn Structured Light. In: Ma, L., Rauterberg, M., Nakatsu, R. (eds) Entertainment Computing – ICEC 2007. ICEC 2007. Lecture Notes in Computer Science, vol 4740. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74873-1_37

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