Increasing Efficiency in Disparity Calculation

  • Jarno Ralli
  • Francisco Pelayo
  • Javier Diaz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4729)


In this paper a trade-off between the computation effort and the accuracy of the resulting disparity map, obtained using interpolation over spatial domain, is presented. The accuracy of the obtained disparity map is presented as the mean squared error calculated over the known disparity ground truth of test images, while efficiency increase is presented in terms of algorithm run-times. Even when reducing the search space for correspondences using epipolar geometry, disparity calculation methods are considered computat- ionally more expensive than interpolation. We show that substantial efficiency increase can be gained using interpolation, in comparison to calculating the dense disparity map directly. As will be shown interpolation also permits us to approximate a disparity value for the occluded pixels. The main contribution of our work is the disparity calculation efficiency increase using interpolation, that fits the sparse disparity map as a 2D surface.


Dense disparity map interpolation visual completion computation efficiency 


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  1. 1.
    Hartley, R., Zimmerman, A.: Multiple View Geometry in Computer Vision, 2nd edn., pp. 204–208. The Press syndicate of the University of Cambridge (2003)Google Scholar
  2. 2.
    Faugeras, O.: Three-Dimensional Computer Vision: A Geometric Viewpoint. MIT Press, Cambridge (1996)Google Scholar
  3. 3.
    Trucco, E., Verri, A.: Introductory Techniques for 3D Computer Vision. Prentice-Hall Inc., Englewood Cliffs (1998)Google Scholar
  4. 4.
    Anderson, B.L., Singh, M., Fleming, R.W.: The Interpolation of Object and Surface Structure. Cognitive Psychology 44, 148–190 (2002)CrossRefGoogle Scholar
  5. 5.
    Wilcox, L., Duke, P.: Spatial and Temporal Properties of Stereoscopic Surface Interpolation. Perception 34, 1325–1338 (2005)CrossRefGoogle Scholar
  6. 6.
    Ohta, Y., Kanade, T.: Stereo by Intra- and Inter-Scanline Search Using Dynamic Programming. IEEE Transactions on Pattern Analysis and Machine Intelligence 7(2), 139–154 (1985)CrossRefGoogle Scholar
  7. 7.
    Middlebury College: Stereo Vision Research Page, URL:
  8. 8.
    Park, J.-I., Um, G.M., Ahn, C.: Virtual Control of Optical Axis of the 3DTV Camera for Reducing Visual Fatigue in Stereoscopic 3DTV. ETRI Journal 26(6), 597–604 (2004)Google Scholar
  9. 9.
    Krüger, N., Felsberg, M.: An Explicit and Compact Coding of Geometric and Structural Information Applied to Stereo Matching. Pattern Recognition Letters 25(8), 849–863 (2004)CrossRefGoogle Scholar
  10. 10.
    Boufama, B., Rastgar, H., Bouakaz, S.: Efficient Surface Interpolation with Occlusion Detection. In: JCIS-2006 Proceedings. Advances in Intelligent Systems Research (2006)Google Scholar
  11. 11.
    Brown, M.Z., Burschka, D., Hager, G.D.: Advances in Computational Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(8), 993–1008 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jarno Ralli
    • 1
  • Francisco Pelayo
    • 1
  • Javier Diaz
    • 1
  1. 1.University Of Granada, Escuela Técnica Superior de Ingenierías Informática y de Telecomunicación, Departamento de Arquitectura y Tecnología de Computadores, Calle Periodista Daniel Saucedo Aranda s/n E-18071 GranadaSpain

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