Abstract
In this paper, we describe a fast object pose estimation method from 3-D to 2-D line correspondences using a perspective camera model. The principle consists in iteratively improving the pose computed with an affine camera model (either weak perspective or paraperspective) to converge, at the limit, to a pose estimation computed with a perspective camera model. Thus, the advantage of the method is to reduce the problem to solving a linear system at each iteration step. The iterative algorithms that we describe in detail in this paper can deal with non coplanar or coplanar object models and have interesting properties both in terms of speed and rate of convergence.
This work has been supported by “Société Aérospatiale” and by DGA/DRET.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
S. Christy and R. Horaud. Euclidean shape and motion from multiple perspective views by affine iterations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(11):1098–1104, November 1996.
D. Dementhon and L.S. Davis. Model-based object pose in 25 lines of code. International Journal of Computer Vision, 15(12):123–141, 1995.
M. Dhome, M. Richetin, J.T. Laprestè, and G. Rives. Determination of the attitude of 3D objects from sigle perspective view. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(12):1265–1278, 1989.
P. Gros. Matching and clustering: Two steps towards automatic model generation in computer vision. In Proceedings of the AAAI Fall Symposium Series: Machine Learning in Computer Vision: What, Why, and How?, Raleigh, North Carolina, USA, pages 40–44, October 1993.
R. Horaud, S. Christy, F. Dornaika, and B. Lamiroy. Object pose: Links between paraperspective and perspective. In Proceedings of the 5th International Conference on Computer Vision, Cambridge, Massachusetts, USA, pages 426–433, Cambridge, Mass., June 1995. IEEE Computer Society Press.
D. Oberkampf, D.F. Dementhon, and L.S. Davis. Iterative pose estimation using coplanar feature points. Computer Vision, Graphics and Image Processing, 63(3):495–511, May 1996.
T.Q. Phong, R. Horaud, A. Yassine, and P.D. Tao. Object pose from 2D to 3D point and line correspondences. In International Journal of Computer Vision, pages 225–243. Kluwer Academic Publishers, July 1995.
C.J. Poelman and T. Kanade. A paraperspective factorization method for shape and motion recovery. In J.O. Eklundh, editor, Proceedings of the 3rd European Conference on Computer Vision, Stockholm, Sweden, pages 97–108, May 1994.
J.S.C. Yuan. A general phogrammetric solution for the determining object position and orientation. IEEE Transactions on Robotics and Automation, 5(2):129–142, 1989.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Christy, S., Horaud, R. (1997). Fast and reliable object pose estimation from line correspondences. In: Sommer, G., Daniilidis, K., Pauli, J. (eds) Computer Analysis of Images and Patterns. CAIP 1997. Lecture Notes in Computer Science, vol 1296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63460-6_147
Download citation
DOI: https://doi.org/10.1007/3-540-63460-6_147
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63460-7
Online ISBN: 978-3-540-69556-1
eBook Packages: Springer Book Archive