Skip to main content
Log in

Motion of points and lines in the uncalibrated case

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

Abstract

In the present paper we address the problem of computing structure and motion, given a set point and/or line correspondences, in a monocular image sequence, when the camera is not calibrated.

Considering point correspondences first, we analyse how to parameterize the retinal correspondences, in function of the chosen geometry: Euclidean, affine or projective geometry. The simplest of these parameterizations is called the FQs-representation and is a composite projective representation. The main result is that considering N+1 views in such a monocular image sequence, the retinal correspondences are parameterized by 11 N−4 parameters in the general projective case. Moreover, 3 other parameters are required to work in the affine case and 5 additional parameters in the Euclidean case. These 8 parameters are “calibration” parameters and must be calculated considering at least 8 external informations or constraints. The method being constructive, all these representations are made explicit.

Then, considering line correspondences, we show how the the same parameterizations can be used when we analyse the motion of lines, in the uncalibrated case. The case of three views is extensively studied and a geometrical interpretation is proposed, introducing the notion of trifocal geometry which generalizes the well known epipolar geometry. It is also discussed how to introduce line correspondences, in a framework based on point correspondences, using the same equations.

Finally, considering the F Qs-representation, one implementation is proposed as a “motion module”, taking retinal correspondences as input, and providing and estimation of the 11 N−4 retinal motion parameters. As discussed in this paper, this module can also estimate the 3D depth of the points up to an affine and projective transformation, defined by the 8 parameters identified in the first section. Experimental results are provided.

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

  • Bar Shalom, Y. and Fortmann, T.E. 1988. Tracking and Data Association Academic Press: Boston.

    Google Scholar 

  • Crowley, J., Bobet, P., and Schmid, C. 1993. Autocalibration by direct observations of objects. Image and Vision Computing, 11.

  • Deriche, R. and Faugeras, O.D. 1990. Tracking line segments. In Proceedings of the 1st ECCV, Antibes, pp. 259–269, Springer-Verlag, Berlin.

    Google Scholar 

  • Deriche, R. and Giraudon, G. 1990. Accurate corner detection: an analytical study. In Proceedings of the 3rd ICCV, Osaka, pp. 66–71.

  • Enciso, R., Viéville, T., and Faugeras, O. 1993. Approximation du changement de focale et de mise au point par une transformation affine à trois paramètres. Technical Report 2071, INRIA.

  • Faugeras, O. 1992. What can be seen in three dimensions with an uncalibrated stereo rig? In 2nd ECCV, Genoa.

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

    Google Scholar 

  • Faugeras, O., Luong, Q.T., and Maybank, S. 1992. Camera selfcalibration: Theory and experiment. In 2nd ECCV, Genoa.

  • Faugeras, O.D., Lustman, F., and Toscani, G. 1987. Motion and structure from point and line matches. In Proceedings of the First International Conference on Computer Vision, London, pp. 25–34.

  • Gill, P.E. and Murray, W. 1978. Algorithms for the solution of nonlinear least squares problem. SIAM Journal on Numerical Analysis, 15:977–992.

    Google Scholar 

  • Guiducci, A. 1988. Corner characterization by differential geometry techniques. Pattern Recognition Letters, 8: 311–318.

    Google Scholar 

  • Hartley, R.I. 1993. Camera calibration using line correspondences. In Proc. DARPA Image Understanding Workshop, pp. 361–366.

  • Hartley, R.I. and Gupta, R. 1993. Computing matched-epipolar projections. In Proceedings of the CVPR'93 Conference, pp. 549–555.

  • Heel, J. 1990. Temporally integrated surface reconstruction. In Proceedings of the 3rd ICCV, Osaka.

  • Huang, T. and Netravali, A. 1990. Linear and polynomial methods in motion estimation. In L. Auslander, T Kailath, and S. Mitter (Eds.), Signal Processing, Part 1: Signal Processing Theory, Springer Verlag.

  • Kanal, L. and Lemmer, J. 1988. Uncertainty in Artificial Intelligence. North Holland Press: Amsterdam.

    Google Scholar 

  • Lavest, J., Rives, G., and Dhome, M. 1993. 3D reconstruction by zooming. In Intelligent Autonomous System, Pittsburg.

  • Liu, Y. and Huang, T.S. 1986. Estimation of rigid body motion using straight line correspondences. In Proceedings Workshop on Motion: Representation and Analysis, Charleston, South California, pp. 47–51.

  • Longuet-Higgins, H.C. 1981. A computer algorithm for reconstructing a scene from two projections. Nature 293: 133–135.

    Google Scholar 

  • Luong, Q., Deriche, R., Faugeras, O., and Papadopoulo, T. 1993. On determining the fundamental matrix: analysis of different methods and experimental results. Technical Report RR-1894, INRIA, Sophia, France.

    Google Scholar 

  • Luong, Q.-T. and Viéville, T. 1994. Canonic representations for the geometries of multiple projective views. In 3rd E.C.C.V., Stockholm.

  • Luong, T. 1992. Matrice Fondamentale et Calibration Visuelle sur l'Environnement. Ph.D. Thesis, Université de Paris-Sud, Orsay.

  • Maybank, S. and Faugeras, O. 1992. A theory of self-calibration of a moving camera. The International Journal of Computer Vision, 8.

  • Mitiche, A., Seida, S., and Aggarwal, J.K. 1986. Interpretation of structure and motion using straight line correspondences. In Proceedings of the 8th ICPR, Paris, France, pp. 1110–1112.

  • Mundy, J. and Zisserman, A. 1992. Geometric Invariance in Computer Vision. MIT Press: Boston.

    Google Scholar 

  • Navab, N., Faugeras, O.D., and Viéville, T. 1993. The critical sets of lines for camera displacement estimation: a mixed Euclidean-projective and constructive approach. In IEEE Proc. Fourth Int'l Conf. Comput. Vision, Berlin, Germany, pp. 713–723.

  • Press, W., Flannery, B., Teukolsky, S., and Vetterling, W. 1988. Numerical Recipes, the Art of Scientific Computing, Cambridge University Press: Cambridge, U.S.A..

    Google Scholar 

  • Quan, L. 1994. Invariants of 6 points from 3 uncalibrated images. In 3rd E.C.C.V., Stockholm.

  • Robert, L. 1992. Perception Stéréoscopique de Courbes et de Surfaces Tridimensionnelles, Application à la Robotique Mobile. PhD Thesis, Ecole Polytechnique, Palaiseau, France.

  • Robert, L. and Faugeras, O. 1993. Relative 3D positionning and 3D convex hull computation from a weakly calibrated stereo pair. In H. Nagel (Ed.), 4th I.C.C.V., Berlin, IEEE Computer Society Press: Los Alamitos, California.

    Google Scholar 

  • Ruymgaart, P.A. and Soong, T.T. 1985 Mathematics of Kalman-Bucy Filtering, Springer Verlag: Berlin.

    Google Scholar 

  • Stephens, M., Blisset, R., Charnley, D., Sparks, E. and Pike, J. 1989. Outdoor vehicle navigation using passive 3D vision. In Computer Vision and Pattern Recognition, IEEE Computer Society Press, pp. 556–562.

  • Thacker, N.A. 1992. On-line calibration of a 4-dof robot head for stereo vision. In British Machine Vision Assoclation Meeting on Active Vision, London.

  • Trivedi, H. 1991. Semi-analytic method for estimating stereo camera geometry from matched points. Image and Vision Computing, 9.

  • Tsai, R.Y. 1989. Synopsis of recent progress on camera calibration for 3D machine vision. Robotics Review, 1: 147–159.

    Google Scholar 

  • Viéville, T. 1994. Autocalibration of visual sensor parameters on a robotic head. Image and Vision Computing, 12.

  • Viéville, T., Facao, P., and Clergue, E. 1993. Building a depth and kinematic 3D-map from visual and inerrial sensors using the vertical cue. In H. Nagel (Ed.), 4th I.C.C.V., Berlin, IEEE Computer Society Press: Los Alamitos, California.

    Google Scholar 

  • Viéville, T., Facao, P., and Clergue, E. 1994. Computation of egomotion using the vertical cue. Machine Vision and Applications, 8.

  • Viéville, T. and Faugeras, O. 1990. Feed forward recovery of motion and structure from a sequence of 2D-lines matches. In S. Tsuji, A. Kak and J.-O. Eklundh (Eds.), Third International Conference on Computer Vision, Osaka, IEEE Computer Society Press: Los Alamitos, California, pp. 517–522.

    Google Scholar 

  • Viéville, T., Zeller, C., and Robert, L. 1994. Using collineations to compute motion and structure in an uncalibrated image sequence, to appear.

  • Willson, R. 1994, Modeling and Calibration of Automated Zoom Lenses. Ph.D. Thesis, Department of Electrical and Computer Engineering, Carnegie Mellon University.

  • Willson, R. and Shafer, S. 1993. What is the center of the image? In IEEE Proc. CVPR'93, New York, pp. 670–671.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Viéville, T., Faugeras, O. & Luong, QT. Motion of points and lines in the uncalibrated case. Int J Comput Vision 17, 7–41 (1996). https://doi.org/10.1007/BF00127817

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00127817

Keywords

Navigation