Novel Stereoscopic View Generation by Image-Based Rendering Coordinated with Depth Information

  • Maiya Hori
  • Masayuki Kanbara
  • Naokazu Yokoya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)

Abstract

This paper describes a method of stereoscopic view generation by image-based rendering in wide outdoor environments. The stereoscopic view can be generated from an omnidirectional image sequence by a light field rendering approach which generates a novel view image from a set of images. The conventional methods of novel view generation have a problem such that the generated image is distorted because the image is composed of parts of several omnidirectional images captured at different points. To overcome this problem, we have to consider the distances between the novel viewpoint and observed real objects in the rendering process. In the proposed method, in order to reduce the image distortion, stereoscopic images are generated considering depth values estimated by dynamic programming (DP) matching using the images that are observed from different points and contain the same ray information in the real world. In experiments, stereoscopic images in wide outdoor environments are generated and displayed.

Keywords

Depth Information Outdoor Environment Depth Estimation View Image Camera Position 
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.

References

  1. 1.
    Moezzi, S. (ed.): Special Issue on Immersive Telepresence, IEEE MultiMedia 4(1), 17–56 (1997)Google Scholar
  2. 2.
    El-Hakim, S.F., Brenner, C., Roth, G.: A Multi-sensor Approach to Creating Accurate Virtual Environments. Journal of Photogrammetry & Remote Sensing 53, 379–391 (1998)CrossRefGoogle Scholar
  3. 3.
    Zhao, H., Shibasaki, R.: Reconstruction of Textured Urban 3D Model by Fusing Ground-Based Laser Range and CCD Images. IEICE Trans. Inf. & Syst. E-83-D(7), 1429–1440 (2000)Google Scholar
  4. 4.
    Asai, T., Kanbara, M., Yokoya, N.: 3D Modeling of Outdoor Environments by Integrating Omnidirectional Range and Color Images. In: Proc. Int. Conf. on 3-D Digital Imaging and Modeling (3DIM), pp. 447–454 (2005)Google Scholar
  5. 5.
    Adelson, E.H., Bergen, J.R.: The Plenoptic Function and the Elements of Early Vision. In: Landy, M., Movshon, J. (eds.) Computer Models of Visual Processing, pp. 3–20. MIT Press, Cambridge (1991)Google Scholar
  6. 6.
    McMillan, L., Bergen, J.: Plenoptic Modeling: An Image-Based Rendering System. In: Proc. SIGGRAPH’95, pp. 39–46 (1995)Google Scholar
  7. 7.
    Gortler, S., Grzeszczuk, R., Szeliski, R., Cohen, M.: The Lumigraph. In: Proc. SIGGRAPH’96, pp. 43–54. ACM Press, New York (1996)Google Scholar
  8. 8.
    Shum, H.Y., He, L.W.: Rendering with Concentric Mosaics. In: Proc. SIGGRAPH’99, pp. 299–306 (1999)Google Scholar
  9. 9.
    Chen, E.: QuickTime VR -An Image-Based Approach to Virtual Environment Navigation. In: Proc. SIGGRAPH’95, pp. 29–38. ACM, New York (1995)Google Scholar
  10. 10.
    Ikeda, S., Sato, T., Kanbara, M., Yokoya, N.: Immersive Telepresence System with a Locomotion Interface Using High-Resolution Omnidirectional Videos. In: Proc. IAPR Conf. on Machine Vision Applications, pp. 602–605 (2005)Google Scholar
  11. 11.
    Yamaguchi, K., Yamazawa, K., Takemura, H., Yokoya, N.: Real-Time Generation and Presentation of View-Dependent Binocular Stereo Images Using a Sequence of Omnidirectional Images. In: Proc. 15th IAPR Int. Conf. on Pattern Recognition (ICPR2000), vol. IV, pp. 589–593 (2000)Google Scholar
  12. 12.
    Ono, S., Ogawara, K., Kagesawa, M., Kawasaki, H., Onuki, M., Honda, K., Ikeuchi, K.: Driving View Simulation Synthesizing Virtual Geometry and Real Images in an Experimental Mixed-Reality Traffic Space. In: Int. Sympo. on Mixed and Augmented Reality, pp. 214–215 (2005)Google Scholar
  13. 13.
    Levoy, M., Hanrahan, P.: Light Field Rendering. In: Proc. SIGGRAPH’96, pp. 31–42. ACM, New York (1996)Google Scholar
  14. 14.
    Bellman, R.: Dynamic Programming. Princeton University Press, Princeton (1957)Google Scholar
  15. 15.
    Bellman, R., Dreyfus, S.: Applied Dynamic Programming. Princeton University Press, Princeton (1962)MATHGoogle Scholar
  16. 16.
    Sakoe, H., Chida, S.: A Dynamic Programming Algorithm Optimization for Spoken Word Recognition. IEEE Trans. on Acoust. Speech and Signal Proc. 26(1), 43–49 (1978)MATHCrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Maiya Hori
    • 1
  • Masayuki Kanbara
    • 1
  • Naokazu Yokoya
    • 1
  1. 1.Graduate School of Information Science, Nara Institute of Science and Technology, 8916–5 Takayama, Ikoma, Nara 630–0192Japan

Personalised recommendations