Virtual Viewpoint Disparity Estimation and Convergence Check for Real-Time View Synthesis

  • In-Yong Shin
  • Yo-Sung Ho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7087)

Abstract

In this paper, we propose a new method for real-time disparity estimation and intermediate view synthesis from stereoscopic images. Some 3D video systems employ both the left and right depth images for virtual view synthesis; however, we estimate only one disparity map at a virtual viewpoint. In addition, we utilize hierarchical belief propagation and convergence check methods to find the global solution rapidly. In order to use the virtual viewpoint disparity map for intermediate view synthesis, we build an occlusion map that describes the occlusion information in the virtual viewpoint region of the reference image. We have also implemented the total system using GPU programming to synthesize virtual viewpoint images in real time.

Keywords

Stereo matching belief propagation CUDA DIBR GPU programming view interpolation 

References

  1. 1.
    ISO/IEC JTC1/SC29/WG11 N6909: Survey of algorithms used for multi-view video coding, MVC (2005)Google Scholar
  2. 2.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 1222–1239 (2000)CrossRefGoogle Scholar
  3. 3.
    Kolmogorov, V., Zabih, R.: Multi-Camera Scene Reconstruction via Graph Cuts. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 82–96. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Sun, J., Zheng, N., Shum, H.: Stereo matching using belief propagation. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 787–800 (2003)CrossRefMATHGoogle Scholar
  5. 5.
    ISO/IEC JTC1/SC29/WG11 M1537: Contribution for 3D Video Test Material of Outdoor Scene (2008)Google Scholar
  6. 6.
    Oh, J., Ma, S., Kuo, C.: Disparity estimation and virtual view synthesis from stereo video. In: Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), New Orleans, LA, USA, pp. 993–996 (2007)Google Scholar
  7. 7.
    Tappen, M., Freeman, W.: Comparison of Graph Cuts with Belief Propagation for Stereo. In: Proc. IEEE Int’l Conf. Computer Vision, vol. 1, pp. 508–515 (2003)Google Scholar
  8. 8.
    Felzenszwalb, P., Huttenlocher, D.: Efficient Belief Propagation for Early Vision. In: CVPR, vol. 1, pp. 261–268 (2004)Google Scholar
  9. 9.
    Yang, Q., Wang, L., Yang, R., Stewenius, H., Nister, D.: Stereo matching with color-weighted correlation, hierarchical belief propagation and occlusion handling. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 492–504 (2009)CrossRefGoogle Scholar
  10. 10.
    Oh, K., Yea, S., Ho, Y.: Hole Filling Method using Depth Based In-painting for View Synthesis in Free Viewpoint Television and 3-D Video. In: Picture Coding Symposium, pp. 39 (1-4) (2009)Google Scholar
  11. 11.
    Oliveira, M., Bowen, B., McKenna, R., Chang, Y.: Fast Digital Image Inpainting. In: Proceedings of the International Conference on Visualization, Imaging and Image Processing, Marbella, Spain (2001)Google Scholar
  12. 12.
    NVIDIA Corporation, CUDA 3.2 Programming Guide (2010), http://www.nvidia.com/cuda_develop.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • In-Yong Shin
    • 1
  • Yo-Sung Ho
    • 1
  1. 1.Gwangju Institute of Science and Technology (GIST)GwangjuKorea

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