3D Model Generation from Image Sequences Using Global Geometric Constraint

  • Masayuki Mukunoki
  • Kazutaka Yasuda
  • Naoki Asada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3804)


This paper describes a method for generating a three-dimensional model from an uncalibrated image sequence taken around an object. Our method is based on feature tracking and minimization of re-projection errors. To cope with mis-matchings in the result of feature tracking, we introduce two types of global geometric constraints. The one is “affine constraint” which imposes the positional relationship between pixels on the images. The other is “depth constraint” which imposes the three-dimensional structure of the object. First, we use the affine constraint to reconstruct the object roughly and then we refine the feature tracking and shape reconstruction using the depth constraint. Experimental results show that our method can automatically generate accurate three-dimensional models from real image sequences.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Matthies, L., Kanade, T., Szeliski, R.: Kalman filter-based algorithms for estimating depth from image sequences. International Journal of Computer Vision 3, 209–239 (1989)CrossRefGoogle Scholar
  2. 2.
    Gimel’farb, G.L., Haralick, R.M.: Terrain reconstruction from multiple views. In: Sommer, G., Daniilidis, K., Pauli, J. (eds.) CAIP 1997. LNCS, vol. 1296, pp. 694–701. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  3. 3.
    Beardsley, P., Torr, P., Zisserman, A.: 3D Model Acquisition from Extended Image Sequences. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 683–695. Springer, Heidelberg (1996)Google Scholar
  4. 4.
    Koch, R., Pollefeys, M., Gool, L.V.: Multi Viewpoint Stereo from Uncalibrated Video Sequences. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 55–71. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  5. 5.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  6. 6.
    Harris, C., Stephens, M.: A Combined Corner and Edge Detector. In: Proc. Alvey Vision Conf., pp. 147–151 (1988)Google Scholar
  7. 7.
    Szeliski, R., Kang, S.B.: Recovering 3D Shape and Motion from Image Streams using Non-Linear Least Squares. In: CVPR, pp. 752–753 (1993)Google Scholar
  8. 8.
    Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle Adjustment – A Modern Synthesis. In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) ICCV-WS 1999. LNCS, vol. 1883, pp. 298–375. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  9. 9.
    Amano, A., Migita, T., Asada, N.: Stable Recovery of Shape and Motion from Partially Tracked Feature Points with Fast Nonlinear Optimization. In: Proc. on Vision Interface, pp. 244–251 (2002)Google Scholar
  10. 10.
    Asada, N., Mukunoki, M., Migita, T., Aoyama, M.: Large Object Shape Recovery from Uncalibrated Camera Motion by Non-Linear Optimization. In: Proc. Int. Conf. on Signal and Image Processing, pp. 151–156 (2004)Google Scholar
  11. 11.
    Zhang, Z.: Determining The Epipolar Geometry And Its Uncertainty: A Review. IJCV 27, 161–195 (1998)CrossRefGoogle Scholar
  12. 12.
    Zhang, Z., Deriche, R., Faugeras, O., Luong, Q.-T.: A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. In: Proc. 3rd Artif. Intell., vol. 78, pp. 87–119 (1995)Google Scholar
  13. 13.
    Kanazawa, Y., Kanatani, K.: Robust Image Matching Preserving Global Consistency. In: Proc. 6th ACCV, pp. 1128–1133 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Masayuki Mukunoki
    • 1
  • Kazutaka Yasuda
    • 1
  • Naoki Asada
    • 1
  1. 1.Department of Intelligent SystemsHiroshima City UniversityHiroshimaJapan

Personalised recommendations