Part of the Springer Series in Information Sciences book series (SSINF, volume 28)


A physically plausible reconstruction unique up to a single unknown scale factor can be obtained from just two different images of the same scene. The differences between the two images contain enough information about the camera position and about the location of the scene points relative to the camera to make the reconstruction possible. The scale factor cannot be recovered from the images alone. It is impossible to tell if the camera is near to a small object or far away from a large object. It is also possible to reconstruct a scene up to a single unknown scale factor using the velocities in an image taken by a moving camera.


Computer Vision Grey Level Projective Space Camera Calibration Algebraic Surface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Faugeras O.D. 1990 On the motion of 3D curves and its relation to optical flow. Proc. First European Conference on Computer Vision (ed. O. Faugeras). Lecture Notes in Computer Science, vol. 427, 107–117. Berlin, Heidelberg and New York: Springer-Verlag.Google Scholar
  2. Fulton W. 1969 Algebraic Curves. Reading, Massachusetts: W.A. Benjamin Inc., Mathematics Lecture Note Series (Reprinted 1974 ).zbMATHGoogle Scholar
  3. Gibson J.J. 1950 The Perception of the Visual World. Boston: Houghton Mifflin.Google Scholar
  4. Golub G.H. & Van Loan C.F. 1983 Matrix Computations. Oxford: North Oxford Publishing Co. Ltd.zbMATHGoogle Scholar
  5. Harris C. 1990 Resolution of the bas-relief ambiguity in structure-from-motion under orthographic projection. BMVC90 Proc. British Machine Vision Conference, Oxford, 67–77.Google Scholar
  6. Horn B.K.P. 1986 Robot Vision. Cambridge, Massachusetts: The MIT Press.Google Scholar
  7. Liu Y. & Huang T.S. 1988 Estimation of rigid body motion using straight line correspondences. Computer Vision, Graphics, and Image Processing 44, 35–57.CrossRefGoogle Scholar
  8. Marr D. 1982 Vision. San Francisco: W. H. Freeman & Co.Google Scholar
  9. Moffitt F.H. & Mikhail E.M. 1980 Photogrammetry (3rd edition). New York: Harper and Row.Google Scholar
  10. Murray D.W. & Buxton B.F. 1990 Experiments in the Machine Interpretation of Visual Motion. Cambridge, Massachusetts: The MIT Press.Google Scholar
  11. Negahdaripour S. & Horn B.K.P. 1987 Direct passive navigation. IEEE Trans. Pattern Analysis and Machine Intelligence 9, 168–176.CrossRefzbMATHGoogle Scholar
  12. Semple J.G. & Kneebone G.T. 1953 Algebraic Projective Geometry. Oxford: Clarendon Press (reprinted 1979).Google Scholar
  13. Semple J.G. & Roth R. 1949 Introduction to Algebraic Geometry. Oxford: Clarendon Press, reprinted 1985.zbMATHGoogle Scholar
  14. Sokolnikoff I.S. & Redheffer R.M. 1966 Mathematics of Physics and Modern Engineering. USA: McGraw-Hill.zbMATHGoogle Scholar
  15. Spetsakis M.E. & Aloimonos J. 1990 Structure from motion using line correspondences. International J. Computer Vision 4, 171–183.CrossRefGoogle Scholar
  16. Walker R. 1962 Algebraic Curves. New York: Dover.zbMATHGoogle Scholar
  17. Waxman A.M., Kamgar-Parsi B. & Subbarao M. 1987 Closed form solutions to image flow equations for 3D structure and motion. International J. Computer Vision 1, 239–258.CrossRefGoogle Scholar
  18. Wolf P.R. 1983 Elements of Photogrammetry (2nd edition). McGraw-Hill.Google Scholar
  19. Wu J. & Wohn K. 1991 On the deformation of image intensity and zero-crossing contours under motion. Computer Vision, Graphics, and Image Processing: Image Understanding 53, 66–75.zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  1. 1.Hirst Research CentreGEC-Marconi LimitedWembley, MiddlesexUK

Personalised recommendations