Skip to main content
Log in

Object Pose: The Link between Weak Perspective, Paraperspective, and Full Perspective

  • Published:
International Journal of Computer Vision Aims and scope Submit manuscript

Abstract

Recently, DeMenthon and Davis (1992, 1995) proposed a method for determining the pose of a 3-D object with respect to a camera from 3-D to 2-D point correspondences. The method consists of iteratively improving the pose computed with a weak perspective camera model to converge, at the limit, to a pose estimation computed with a perspective camera model. In this paper we give an algebraic derivation of DeMenthon and Davis' method and we show that it belongs to a larger class of methods where the perspective camera model is approximated either at zero order (weak perspective) or first order (paraperspective). We describe in detail an iterative paraperspective pose computation method for both non coplanar and coplanar object points. We analyse the convergence of these methods and we conclude that the iterative paraperspective method (proposed in this paper) has better convergence properties than the iterative weak perspective method. We introduce a simple way of taking into account the orthogonality constraint associated with the rotation matrix. We analyse the sensitivity to camera calibration errors and we define the optimal experimental setup with respect to imprecise camera calibration. We compare the results obtained with this method and with a non-linear optimization method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Aloimonos, Y. 1990. Perspective approximations. Image and Vision Computing, 8(3):177-192.

    Google Scholar 

  • Christy, S. and Horaud, R. 1996. Euclidean shape and motion from multiple perspective views by affine iterations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(11):1098-1104.

    Google Scholar 

  • DeMenthon, D.F. 1993. De la Vision Artificielle àla RéalitéSynthétique: Système d'interaction avec un ordinateur utilisant l'analyse d'images vidéo. Ph.D. thesis, Universitè Joseph Fourier-Grenoble I, Laboratoire TIMC/IMAG.

  • DeMenthon, D.F. and Davis, L.S. 1992. Model-based object pose in 25 lines of code. In G. Sandini (Ed.), Computer Vision-ECCV 92, Proceedings Second European Conference on Computer Vision, Santa Margherita Ligure, Springer Verlag, pp. 335-343.

    Google Scholar 

  • DeMenthon, D.F. and Davis, L.S. 1995. Model-based object pose in 25 lines of code. International Journal of Computer Vision, 15(1/2):123-141.

    Google Scholar 

  • Dhome, M., Richetin, M., Lapreste, J.T., and Rives, G. 1989. Determination of the attitude of 3D objects from a single perspective view. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(12):1265-1278.

    Google Scholar 

  • Faugeras, O.D. 1993. Three Dimensional Computer Vision: A Geometric Viewpoint. MIT Press: Boston.

    Google Scholar 

  • Fischler, M.A. and Bolles, R.C. 1981. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395.

    Google Scholar 

  • Gros, P. 1993. 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, pp. 40-44.

    Google Scholar 

  • Haralick, R.B., Joo, H., Lee, C.-N., Zhuang, X., Vaidya, V.G., and Kim, M.B. 1989. Pose estimation from corresponding point data. IEEE Transactions on Systems, Man, and Cybernetics, 19(6):1426-1445.

    Google Scholar 

  • Horaud, R., Conio, B., Leboulleux, O., and Lacolle, B. 1989. An analytic solution for the perspective 4-point problem. Computer Vision, Graphics, and Image Processing, 47(1):33-44.

    Google Scholar 

  • Horaud, R., Dornaika, F., Bard, C., and Espiau, B. 1995. Visually guided object grasping. Technical report, INRIA. Submitted to IEEE Trans. on Robotics & Automation.

  • Lowe, D. 1991. Fitting parameterized three-dimensional models to images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(5):441-450.

    Google Scholar 

  • Oberkampf, D., DeMenthon, D.F., and Davis, L.S. 1993. Iterative pose estimation using coplanar feature points. In Proceedings Computer Vision and Pattern Recognition, New York. IEEE Computer Society Press: Los Alamitos, CA, pp. 626-627.

    Google Scholar 

  • Phong, T.Q., Horaud, R., Yassine, A., and Pham, D.T. 1993. Optimal estimation of object pose from a single perspective view. In Proceedings Fourth International Conference on Computer Vision, Berlin, Germany. IEEE Computer Society Press, Los Alamitos, CA, pp. 534-539.

    Google Scholar 

  • Phong, T.Q., Horaud, R., Yassine, A., and Pham, D.T. 1995. Object pose from 2-D to 3-D point and line correspondences. International Journal of Computer Vision, 15(3):225-243.

    Google Scholar 

  • Poelman, C.J. and Kanade, T. 1994. A paraperspective factorization method for shape and motion recovery. In Jan-Olof Eklundh, (Ed.), Computer Vision-ECCV 94, Proceedings Third European Conference on Computer Vision, Stockholm, Springer Verlag, Vol. 2, pp. 97-108.

    Google Scholar 

  • Tsai, R.Y. 1987. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Journal of Robotics and Automation, RA-3(4):323-344.

    Google Scholar 

  • Yuan, J.S.-C. 1989. A general photogrammetric method for determining object position and orientation. IEEE Transactions on Robotics and Automation, 5(2):129-142.

    Google Scholar 

Download references

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Horaud, R., Dornaika, F. & Lamiroy, B. Object Pose: The Link between Weak Perspective, Paraperspective, and Full Perspective. International Journal of Computer Vision 22, 173–189 (1997). https://doi.org/10.1023/A:1007940112931

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1007940112931

Navigation