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Advances in 3D Camera: Time-of-Flight vs. Active Triangulation

  • Daesik Kim
  • Sukhan Lee
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 193)

Abstract

Over the last decade, numerous 3D camera techniques have been proposed and advanced dramatically. One main approach is time-of-flight (TOF) and the other is active triangulation. Each has its own strengths and weaknesses. In this paper, we overview the principle of each method and compare the advantages and disadvantages in detail, and introduce several commercially available 3D cameras and their characteristics.

Keywords

Depth Resolution Stereo Camera Block Match Algorithm Front Portion Short Light Pulse 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Kahlmann, T., Remondino, F., Ingensand, H.: Proceedings of the ISPRS Commission V Symposium ’Image Engineering and Vision Metrology’, pp. 136–141 (2006)Google Scholar
  2. 2.
    Lange, R.: 3D time-of-flight distance measurement with custom solid-state image sensors in CMOS/CCD-technology. Ph.D. thesis, University of Siegen (2000)Google Scholar
  3. 3.
    Oggier, T., Lehmann, M., Kaufmann, R., Schweizer, M., Richter, M., Metzler, P., Lang, G., Lustenberger, F., Blanc, N.: Proceedings of SPIE: Specific Applications: Sensors and Medical OpticsGoogle Scholar
  4. 4.
    Blanc, N., Oggier, T., Gruener, G., Weingarten, J., Codourey, A., Seitz, P.: Proceedings of IEEE Sensors, pp. 471–474 (2004)Google Scholar
  5. 5.
    Oggier, T., Lustenberger, F., Blanc, N.: Miniature 3D TOF Camera for Real-Time Imaging. In: André, E., Dybkjær, L., Minker, W., Neumann, H., Weber, M. (eds.) PIT 2006. LNCS (LNAI), vol. 4021, pp. 212–216. Springer, Heidelberg (2006)Google Scholar
  6. 6.
    Gokturk, S., Yalcin, H., Bamji, C.: Conference on Computer Vision and Pattern Recognition Workshop, p. 35 (2004)Google Scholar
  7. 7.
    Iddan, G., Yahav, G.: Proceedings of SPIE: Videometrics and Optical Methods for 3D Shape Measurements, pp. 48–55 (2001)Google Scholar
  8. 8.
    Gvili, R., Kaplan, A., Ofek, E., Yahav, G.: Proceedings of SPIE Video-Based Image Techniques and Emerging Work, pp. 564–574 (2003)Google Scholar
  9. 9.
    Medina, A., Gaya, F., del Pozo, F.: Journal of the Optical Society of America A 23(4), 800 (2006)Google Scholar
  10. 10.
    Yahav, G., Iddan, G., Mandelboum, D.: Digest of Technical Papers of International Conference on Consumer Electronics, pp. 1–2 (2007)Google Scholar
  11. 11.
    Davis, J., Gonzalez-banos, H.: IEEE International Workshop on Projector-Camera Systems (2003)Google Scholar
  12. 12.
    Konolige, K.: Proceedings of IEEE International Conference on Robotics and Automation, pp. 148–155 (2010)Google Scholar
  13. 13.
    Sazbon, D., Zalevsky, Z., Rivlin, E.: Pattern Recognition Letters.  26(11), 1772 (2005)Google Scholar
  14. 14.
    García, J., Zalevsky, Z., García-Martínez, P., Ferreira, C., Teicher, M., Beiderman, Y.: Applied Optics 47(16), 3032 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringSungkyunkwan UniversitySuwonKorea
  2. 2.Department of Electrical and Computer Engineering and Department of Interaction ScienceSungkyunkwan UniversitySuwonKorea

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