Generation of Temporally Consistent Depth Maps Using Nosie Removal from Video

  • Olgierd Stankiewicz
  • Krzysztof Wegner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6375)


This paper presents a novel approach for providing depth maps that are temporally consistent. Temporal consistency is attained by noise removal from video. Presented approach was evaluated with use of a simple noise reduction technique and state-of-the-are depth estimation algorithm. Experiments on depth-based synthesis of standard multi-view test video sequences have been performed and yielded both subjective and objective results. These results provide evidence that the proposed approach increase temporal consistency of estimated depth maps.


depth map estimation temporal consistency temporal noise removal 


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  1. 1.
    Domański, M., Klimaszewski, K., Konieczny, J., Kurc, M., Luczak, A., Stankiewicz, O., Wegner, K.: An experimental Free-view Television System. In: IP&C, Bydgoszcz, Poland (September 2009)Google Scholar
  2. 2.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Intern. Journal of Comp. Vision 47, 742 (2002)Google Scholar
  3. 3.
    Scott Larsen, E., et al.: Temporally Consistent Reconstruction from Multiple Video Streams Using Enhanced Belief Propagation. In: ICCV 2007 (2007)Google Scholar
  4. 4.
    Pedro, F.F., Daniel, P.H.: Efficient Belief Propagation for Early Vision. International Journal of Comp. Vision 70(1) (October 2006)Google Scholar
  5. 5.
    Tao, H., Sawhney, H.S., Kumar, R.: Dynamic Depth Recovery from Multiple Synchronized Video Streams. In: ICIP 2003 (2003)Google Scholar
  6. 6.
    Vaseghi, S.V.: Advanced Digital Signal Processing and Noise Reduction, 3rd edn. John Wiley & Sons, Chichester (2006) ISBN: 978-0-470-09495-2Google Scholar
  7. 7.
    Van Etten, W.C.: Introduction to Random Signals and Noise. John Wiley & Sons, Chichester (2006) ISBN 0-470-02411-9Google Scholar
  8. 8.
    Dugad, R., Ahuja, N.: Video denoising by combining Kalman and Wiener estimates. In: Proceedings of ICIP, pp. 152–156 (1999)Google Scholar
  9. 9.
    Stankiewicz, O., Wegner, K., Wildeboer, M.: A soft–segmentation matching in Depth Estimation Reference Software (DERS) 5.0, ISO/IEC JTC1/SC29/WG11 MPEG/M17049, Xian, China (October 2009)Google Scholar
  10. 10.
    Domański, M., Grajek, T., Klimaszewski, K., Kurc, M., Stankiewicz, O., Stankowski, J., Wegner, K.: Poznan Multiview Video Test Sequences and Camera Parameters, ISO/IEC JTC1/SC29/WG11 MPEG/M17050, Xian, China (October 2009)Google Scholar
  11. 11.
    Feldmann, I., Mueller, M., Zilly, F., Tanger, R., Mueller, K., Smolic, A., Kauff, P., Wiegand, T.: HHI Test Material for 3D Video. MPEG/M15413, Archamps, France (April 2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Olgierd Stankiewicz
    • 1
  • Krzysztof Wegner
    • 1
  1. 1.Chair of Multimedia Telecommunications and MicroelectronicsPoznań University of TechnologyPoznańPoland

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