Advertisement

Adaptive Metric Registration of 3D Models to Non-rigid Image Trajectories

  • Alessio Del Bue
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6313)

Abstract

This paper addresses the problem of registering a 3D model, represented as a cloud of points lying over a surface, to a set of 2D deforming image trajectories in the image plane. The proposed approach can adapt to a scenario where the 3D model to register is not an exact description of the measured image data. This results in finding the best 2D–3D registration, given the complexity of having both 2D deforming data and a coarse description of the image observations. The method acts in two distinct phases. First, an affine step computes a factorization for both the 2D image data and the 3D model using a joint subspace decomposition. This initial solution is then upgraded by finding the best projection to the image plane complying with the metric constraints given by a scaled orthographic camera. Both steps are computed efficiently in closed-form with the additional feature of being robust to degenerate motions which may possibly affect the 2D image data (i.e. lack of relevant rigid motion). Moreover, we present an extension of the approach for the case of missing image data. Synthetic and real experiments show the robustness of the method in registration tasks such as pose estimation of a talking face using a single 3D model.

Keywords

Rotation Error Rigid Registration Structure From Motion Reprojection Error Generalise Singular Value Decomposition 
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.

Supplementary material

978-3-642-15558-1_7_MOESM1_ESM.wmv (7.7 mb)
Electronic Supplementary Material (7,913 KB)

References

  1. 1.
    Bartoli, A., Gay-Bellile, V., Castellani, U., Peyras, J., Olsen, S., Sayd, P.: Coarse-to-Fine Low-Rank Structure-from-Motion. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, pp. 1–8 (2008)Google Scholar
  2. 2.
    Brand, M.: A direct method for 3D factorization of nonrigid motion observed in 2D. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, San Diego, California, pp. 122–128 (2005)Google Scholar
  3. 3.
    Bregler, C., Hertzmann, A., Biermann, H.: Recovering non-rigid 3D shape from image streams. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head, South Carolina, pp. 690–696 (June 2000)Google Scholar
  4. 4.
    Cootes, T.F., Taylor, C.J.: Active shape models. In: Proc. British Machine Vision Conference, pp. 265–275 (1992)Google Scholar
  5. 5.
    Del Bue, A.: A factorization approach to structure from motion with shape priors. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, pp. 1–8 (2008)Google Scholar
  6. 6.
    Hansen, P.: Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion. Society for Industrial Mathematics (1998)Google Scholar
  7. 7.
    Marques, M., Costeira, J.P.: Estimating 3D shape from degenerate sequences with missing data. Computer Vision and Image Understanding (2008)Google Scholar
  8. 8.
    Shen, D., Davatzikos, C.: An adaptive-focus deformable model using statistical and geometricinformation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 906–913 (2000)CrossRefGoogle Scholar
  9. 9.
    Stegmann, M.B., Ersbøll, B.K., Larsen, R.: FAME – a flexible appearance modelling environment. IEEE Trans. on Medical Imaging 22(10), 1319–1331 (2003)CrossRefGoogle Scholar
  10. 10.
    Sturm, J.: Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones. Optimization Methods and Software 11(1), 625–653 (1999)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Tomasi, C., Kanade, T.: Shape and motion from image streams under orthography: A factorization approach. International Journal of Computer Vision 9(2), 137–154 (1992)CrossRefGoogle Scholar
  12. 12.
    Torresani, L., Hertzmann, A., Bregler., C.: Non-rigid structure-from-motion: Estimating shape and motion with hierarchical priors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 878–892 (2008)Google Scholar
  13. 13.
    Xiao, J., Baker, S., Matthews, I., Kanade, T.: Real-time combined 2d+3d active appearance models. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Washington D.C., vol. 2, pp. 535–542 (June 2004)Google Scholar
  14. 14.
    Xiao, J., Georgescu, B., Zhou, X., Comaniciu, D., Kanade, T.: Simultaneous Registration and Modeling of Deformable Shapes. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, New York, NY, pp. 2429–2436 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alessio Del Bue
    • 1
  1. 1.Istituto Italiano di TecnologiaGenovaItaly

Personalised recommendations