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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 51, Iss. 7 — Mar. 1, 2012
  • pp: 936–948

Robust camera pose estimation from unknown or known line correspondences

Xiaohu Zhang, Zheng Zhang, You Li, Xianwei Zhu, Qifeng Yu, and Jianliang Ou  »View Author Affiliations


Applied Optics, Vol. 51, Issue 7, pp. 936-948 (2012)
http://dx.doi.org/10.1364/AO.51.000936


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Abstract

We address the model-to-image registration problem with line features in the following two ways. (a) We present a robust solution to simultaneously recover the camera pose and the three-dimensional-to-two-dimensional line correspondences. With weak pose priors, our approach progressively verifies the pose guesses with a Kalman filter by using a subset of recursively found match hypotheses. Experiments show our method is robust to occlusions and clutter. (b) We propose a new line feature based pose estimation algorithm, which iteratively optimizes the objective function in the object space. Experiments show that the algorithm has strong robustness to noise and outliers and that it can attain very accurate results efficiently.

© 2012 Optical Society of America

OCIS Codes
(000.4430) General : Numerical approximation and analysis
(100.2960) Image processing : Image analysis
(150.1135) Machine vision : Algorithms

ToC Category:
Image Processing

History
Original Manuscript: October 17, 2011
Revised Manuscript: December 7, 2011
Manuscript Accepted: December 17, 2011
Published: February 28, 2012

Citation
Xiaohu Zhang, Zheng Zhang, You Li, Xianwei Zhu, Qifeng Yu, and Jianliang Ou, "Robust camera pose estimation from unknown or known line correspondences," Appl. Opt. 51, 936-948 (2012)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-51-7-936


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References

  1. M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24, 381–395 (1981). [CrossRef]
  2. P. David, D. Dementhon, R. Duraiswami, and H. Samet, “SoftPOSIT: Simultaneous pose and correspondence determination,” Int. J. Comput. Vis. 59, 259–284 (2004). [CrossRef]
  3. P. David, D. Dementhon, R. Duraiswami, and H. Samet, “Simultaneous pose and correspondence determination using line features,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003 (IEEE, 2003), pp. II-424–II-431.
  4. F. Moreno-Noguer, V. Lepetit, and P. Fua, “Pose priors for simultaneously solving alignment and correspondence,” in Proceedings of the 10th European Conference on Computer Vision: Part II (Springer-Verlag, 2008), pp. 405–418.
  5. M. Dhome, M. Richetin, J. thierry Lapreste, and G. Rives, “Determination of the attitude of 3-D objects from a single perspective view,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 1265–1278 (1989). [CrossRef]
  6. D. DeMenthon and L. Davis, “Model-based object pose in 25 lines of code,” Int. J. Comput. Vis. 15, 123–141 (1995). [CrossRef]
  7. R. Horaud, F. Dornaika, B. Lamiroy, and S. Christy, “Object pose: the link between weak perspective, paraperspective and full perspective,” Int. J. Comput. Vis. 22, 173–189 (1997). [CrossRef]
  8. S. Christy and R. Horaud, “Iterative pose computation from line correspondences,” Comput. Vis. Image Understand. 73, 137–144 (1999). [CrossRef]
  9. C.-P. Lu, G. D. Hager, and E. Mjolsness, “Fast and globally convergent pose estimation from video images,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 610–622 (2000). [CrossRef]
  10. A. Ansar and K. Daniilidis, “Linear pose estimation from points or lines,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 578–589 (2003). [CrossRef]
  11. A. Castro, Y. Frauel, E. Tepichin, and B. Javidi, “Pose estimation from a two-dimensional view by use of composite correlation filters and neural networks,” Appl. Opt. 42, 5882–5890 (2003). [CrossRef]
  12. F. Moreno-Noguer, V. Lepetit, and P. Fua, “Accurate non-iterative O(n) solution to the PnP problem,” in Proceedings of the IEEE 11th International Conference on Computer Vision, 2007 (IEEE, 2007), pp. 1–8.
  13. W. E. L. Grimson, “Object recognition by computer: the role of geometric constraints,” Int. J. Comput. Vis. 31, 350–504 (1990).
  14. J. S. Beis and D. G. Lowe, “Indexing without invariants in 3D object recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 1000–1015 (1999). [CrossRef]
  15. Y. Lamdan and H. J. Wolfson, “Geometric hashing: a general and efficient model-based recognition scheme,” in Proceedings of the Second International Conference on Computer Vision (IEEE, 1988), pp. 238–249.
  16. P. Wunsch and G. Hirzinger, “Registration of cad models to images by iterative inverse perspective matching,” in Proceedings of the 13th International Conference on Pattern Recognition (IEEE, 1996), pp. 78–83.
  17. J. R. Beveridge and E. M. Riseman, “Optimal geometric model matching under full 3d perspective,” Comput. Vis. Image Understand. 61, 351–364 (1995). [CrossRef]
  18. F. Jurie, “Solution of the simultaneous pose and correspondence problem using gaussian error model,” Comput. Vis. Image Understand. 73, 357–373 (1999). [CrossRef]
  19. R. M. Haralick, H. Joo, C.-N. Lee, X. Zhuang, V. G. Vaidya, and M. B. Kim, “Pose estimation from corresponding point data,” IEEE Trans. Syst. Man Cybern. 19, 1426–1446 (1989). [CrossRef]
  20. R. Horaud, F. Dornaika, B. Lamiroy, S. Christy, “Object pose: the link between weak perspective, paraperspective, and full perspective,” Int. J. Comput. Vis. 22, 173–189 (1997). [CrossRef]
  21. L. Quan and Z. Lan, “Linear N-point camera pose determination,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 774–780 (1999). [CrossRef]
  22. H. H. Chen, “Pose determination from line-to-plane correspondences: existence condition and closed-form solutions,” IEEE Trans. Pattern Anal. Mach. Intell. 13, 530–541 (1991). [CrossRef]
  23. Y. Liu, T. S. Huang, and O. D. Faugeras, “A linear algorithm for motion estimation using straight line correspondences,” Comput. Vis. Graph. Image Process. 44, 35–57 (1988). [CrossRef]
  24. Y. Liu, T. S. Huang, and L. D. Faugeras, “Determination of camera location from 2-D to 3-D line and point correspondences,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 28–37 (1990). [CrossRef]
  25. C.-N. Lee and R. M. Haralick, “Statistical estimation for exterior orientation from line-to-line correspondences,” Image Vis. Comput. 14, 379–388 (1996). [CrossRef]
  26. R. Kumar and A. R. Hanson, “Robust methods for estimating pose and a sensitivity analysis,” Comput. Vis. Graph. Image Process. 60, 313–342 (1994). [CrossRef]
  27. T. Q. Phong, R. Horaud, A. Yassine, and P. D. Tao, “Object pose from 2D to 3D point and line correspondences,” Int. J. Comput. Vis. 15, 225–243 (1995). [CrossRef]
  28. N. Navab and O. Faugeras, “Monocular pose determination from lines: critical sets and maximum number of solutions,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 1993), pp. 254–260.
  29. S. Umeyama, “Least-squares estimation of transformatio parameters between two point patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 13, 376–380 (1991). [CrossRef]
  30. B. K. P. Horn, H. Hilden, and S. Negahdaripour, “Closed-form solution of absolute orientation using orthonormal matrices,” J. Opt. Soc. Am. 5, 1127–1135 (1988). [CrossRef]
  31. H. Wuest, F. Vial, and D. Stricker, “Adaptive line tracking with multiple hypotheses for augmented reality,” in Proceedings of the 4th IEEE/ACM International Symposium on Mixed and Augmented Reality (IEEE, 2005), pp. 62–69.

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