The Visual Computer

, Volume 26, Issue 11, pp 1407–1420 | Cite as

Evaluation of texture registration by epipolar geometry

  • Ioan ClejuEmail author
  • Dietmar Saupe
Original Article


In the process of digitizing the geometry and appearance of 3D objects, texture registration is a necessary step that solves the 2D–3D mapping between the 2D texture images and the 3D geometric model. For evaluation of texture registration with ground truth, accurate datasets can be obtained with a complex setup consisting of calibrated geometry and texture capture devices. We do not have any knowledge of such evaluation performed; current evaluations reflect, at their best, the precision achieved by the algorithms, but fail to identify a possible bias. We propose a new evaluation measure based on the epipolar geometry of texture image pairs, with the advantage that the ground truth can be extracted solely from the texture images, independent of the 3D acquisition. We developed a noise model suitable to our purpose and analysed three distance measures based on epipolar geometry, well known in the computer vision community, to find new theoretical and experimental results. Finally, using the proposed framework, we evaluated a texture registration algorithm based on mutual information and found that its accuracy was under half-pixel.


Texture registration Epipolar geometry Epipolar distances Experimental evaluation Mutual information 


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  1. 1.
    3D surface acquisition project. Website (2009).
  2. 2.
    Bouguet, J.Y.: Camera calibration toolbox for Matlab. Website (2009).
  3. 3.
    Clarkson, M.J., Rueckert, D., Hill, D.L.G., Hawkes, D.J.: Using photo-consistency to register 2D optical images of the human face to a 3D surface model. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1266–1280 (2001) CrossRefGoogle Scholar
  4. 4.
    Cleju, I.: Texture registration for 3D models. Ph.D. thesis, University of Konstanz (2008) Google Scholar
  5. 5.
    Cleju, I., Saupe, D.: Stochastic optimization of multiple texture registration using mutual information. In: LNCS Pattern Recognition: Proceedings Annual Symp. of the German Association for Pattern Recognition DAGM, vol. 4713, pp. 517–526. Springer, Berlin (2007) Google Scholar
  6. 6.
    Faugeras, O., Luong, Q.T.: The Geometry of Multiple Images. MIT, Cambridge (2001) zbMATHGoogle Scholar
  7. 7.
    Hartley, R., Sturm, P.: Triangulation. Comput. Vis. Image Underst. 68(2), 146–157 (1997) CrossRefGoogle Scholar
  8. 8.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, vol. 1. Cambridge University Press, Cambridge (2000) zbMATHGoogle Scholar
  9. 9.
    Leedan, Y., Meer, P.: Heteroscedastic regression in computer vision: Problems with bilinear constraint. Int. J. Comput. Vis. 37(2), 127–150 (2000) zbMATHCrossRefGoogle Scholar
  10. 10.
    Lensch, H.P.A., Heidrich, W., Seidel, H.P.: A silhouette-based algorithm for texture registration and stitching. Graph. Models 63(4), 245–262 (2001) zbMATHCrossRefGoogle Scholar
  11. 11.
    Ma, Y., Koëcká, J., Sastry, S.: Optimization criteria and geometric algorithms for motion and structure estimation. Int. J. Comput. Vis. 44, 219–249 (2001) zbMATHCrossRefGoogle Scholar
  12. 12.
    Neugebauer, P.J., Klein, K.: Texturing 3D models of real world objects from multiple unregistered photographic views. Comput. Graph. Forum 3(18), 245–256 (1999) CrossRefGoogle Scholar
  13. 13.
    Seitz, S., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: Proceedings IEEE Conference Computer Vision Pattern Recognition, pp. 519–526. IEEE (2006) Google Scholar
  14. 14.
    Torr, P.H.S., Murray, D.W.: The development and comparison of robust methods for estimating the fundamental matrix. Int. J. Comput. Vis. 24(3), 271–300 (1997) CrossRefGoogle Scholar
  15. 15.
    Troccoli, A.J., Allen, P.K.: Shadow based texture registration for 3D modeling of outdoor scenes. Mach. Vis. Appl. 18(2), 65–72 (2007) CrossRefGoogle Scholar
  16. 16.
    Van de Kraats, E.B., Penney, G.P., Tomazevic, D., van Walsum, T., Niessen, W.J.: Standardized evaluation methodology for 2D–3D registration. IEEE Trans. Med. Imaging 24(9), 1177–1190 (2005) CrossRefGoogle Scholar
  17. 17.
    Zhang, Z.: Determining the epipolar geometry and its uncertainty: A review. Int. J. Comput. Vis. 27, 161–195 (1998) CrossRefGoogle Scholar
  18. 18.
    Zhang, Z.: On the optimization criteria used in two-view motion analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(7), 717–729 (1998) CrossRefGoogle Scholar
  19. 19.
    Zhang, Z.: Flexible camera calibration by viewing a plane from unknown orientations. In: Proceedings International Conference on Computer Vision (ICCV’99), vol. 1, pp. 666–673. IEEE Computer Society (1999) Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Oxford Metrics Group (YottaDCL)Leamington SpaUK
  2. 2.Department of Computer and Information ScienceUniversity of KonstanzKonstanzGermany

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