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Adaptive Non-rigid Registration and Structure from Motion from Image Trajectories

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Abstract

This paper addresses the problem of registering a known 3D model to a set of 2D deforming image trajectories. 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 a 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). A further extension of the approach allows to compute the full 3D deformations of the shape given the first initial (rigid) registration. This step results in solving a Non-rigid Structure from Motion (NRSfM) problem using the 3D known shape as a prior. Experimental results show the robustness of the method in registration tasks such as pose estimation and 3D reconstruction when degenerate image motion is present.

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References

  • Marques, M., & Costeira, J. (2009). Estimating 3d shape from degenerate sequences with missing data. Computer Vision and Image Understanding, 113(2), 261–272.

    Article  Google Scholar 

  • Akhter, I., Sheikh, Y., & Khan, S. (2009). In defense of orthonormality constraints for nonrigid structure from motion. In Proc. IEEE conference on computer vision and pattern recognition, Miami, Florida.

    Google Scholar 

  • Bartoli, A., Pizarro, D., & Loog, M. (2010). Stratified generalized procrustes analysis. In Proceedings of the British machine vision conference (pp. 70.1–70.10).

    Google Scholar 

  • Bartoli, A., Gay-Bellile, V., Castellani, U., Peyras, J., Olsen, S., & Sayd, P. (2008). Coarse-to-fine low-rank structure-from-motion. In Proc. IEEE conference on computer vision and pattern recognition, Anchorage, Alaska (pp. 1–8).

    Google Scholar 

  • Basri, R., Jacobs, D., & Kemelmacher, I. (2007). Photometric stereo with general, unknown lighting. International Journal of Computer Vision, 72(3), 239–257.

    Article  Google Scholar 

  • Björck, Å. (1996). Numerical methods for least squares problems. SIAM: Philadelphia.

    Book  MATH  Google Scholar 

  • Brand, M. (2005). A direct method for 3d factorization of nonrigid motion observed in 2d. In Proc. IEEE conference on computer vision and pattern recognition (pp. 122–128), San Diego, California.

    Google Scholar 

  • Bregler, C., Hertzmann, A., & Biermann, H. (2000). 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).

    Google Scholar 

  • Del Bue, A. (2008). 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).

    Google Scholar 

  • Del Bue, A. (2010). Adaptive metric registration of 3d models to non-rigid image trajectories. In K. Daniilidis, P. Maragos, & N. Paragios (Eds.), Lecture Notes in Computer Science: Vol. 6313. 11th European conference on computer vision (ECCV 2010), Crete, Greece (pp. 87–100). Berlin: Springer.

    Chapter  Google Scholar 

  • Del Bue, A., Lladó, X., & Agapito, L. (2006). Non-rigid metric shape and motion recovery from uncalibrated images using priors. In Proc. IEEE conference on computer vision and pattern recognition, New York, NY (pp. 1191–1198).

    Google Scholar 

  • Del Bue, A., Smeraldi, F., & Agapito, L. (2007). Non-rigid structure from motion using ranklet–based tracking and non-linear optimization. Image and Vision Computing, 25(3), 297–310.

    Article  Google Scholar 

  • Del Bue, A., Xavier, J., Agapito, L., & Paladini, M. (2012). Bilinear modelling via augmented Lagrange multipliers (BALM). IEEE Trans. Pattern Anal. Machine Intell. 34(8) 1496–1508.

    Article  Google Scholar 

  • Forsyth, D., Ioffe, S., & Haddon, J. (1999). Bayesian structure from motion. In Proc. 7th international conference on computer vision 1, Kerkyra, Greece (p. 660).

    Chapter  Google Scholar 

  • Hansen, P. (1998). Rank-deficient and discrete Ill-posed problems: numerical aspects of linear inversion. Society for Industrial Mathematics.

    Book  Google Scholar 

  • Olsen, S., & Bartoli, A. (2008). Implicit non-rigid structure-from-motion with priors. Journal of Mathematical Imaging and Vision, 31(2), 233–244.

    Article  MathSciNet  Google Scholar 

  • Solem, J., & Kahl, F. (2005). Surface reconstruction using learned shape models. Advances in neural information processing systems 17

  • Stegmann, M. B., Ersbøll, B. K., & Larsen, R. (2003). FAME—a flexible appearance modelling environment. IEEE Transactions on Medical Imaging, 22(10), 1319–1331.

    Article  Google Scholar 

  • Sturm, J. (1999). Using SeDuMi 1.02, a Matlab toolbox for optimization over symmetric cones. Optimization Methods & Software, 11(1), 625–653.

    Article  MathSciNet  Google Scholar 

  • Tomasi, C., & Kanade, T. (1992). Shape and motion from image streams under orthography: a factorization approach. International Journal of Computer Vision, 9(2), 137–154.

    Article  Google Scholar 

  • Torresani, L., Hertzmann, A., & Bregler, C. (2008). Non-rigid structure-from-motion: estimating shape and motion with hierarchical priors. In IEEE transactions on pattern analysis and machine intelligence (pp. 878–892).

    Google Scholar 

  • Triggs, B., McLauchlan, P., Hartley, R. I., & Fitzgibbon, A. (2000). Bundle adjustment—a modern synthesis. In W. Triggs, A. Zisserman, & R. Szeliski (Eds.), Vision algorithms: theory and practice, LNCS (pp. 298–375). Berlin: Springer citeseer.nj.nec.com/triggs00bundle.html.

    Chapter  Google Scholar 

  • Xiao, J., Baker, S., Matthews, I., & Kanade, T. (2004). Real-time combined 2d+3d active appearance models. In Proceedings of the IEEE conference on computer vision and pattern recognition, vol. 2 (pp. 535–542).

    Google Scholar 

  • Xiao, J., Chai, J., & Kanade, T. (2006). A closed-form solution to non-rigid shape and motion recovery. International Journal of Computer Vision, 67(2), 233–246.

    Article  Google Scholar 

  • Yezzi, A. J., & Soatto, S. (2003). Deformation: deforming motion, shape average and the joint registration and approximation of structures in images. International Journal of Computer Vision, 53(2), 153–167.

    Article  Google Scholar 

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Acknowledgements

This work was partially supported by Fundação para a Ciência e a Tecnologia (ISR/IST pluriannual funding) through the POS_Conhecimento Program (include FEDER funds) and grant PTDC/EEA-ACR/72201/2006, “MODI—3D Models from 2D Images”. E. Muñoz, J. Xiao and J. Peyras kindly made available sequences used in the experimental section.

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Correspondence to Alessio Del Bue.

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Del Bue, A. Adaptive Non-rigid Registration and Structure from Motion from Image Trajectories. Int J Comput Vis 103, 226–239 (2013). https://doi.org/10.1007/s11263-012-0577-9

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