Abstract
One of the most powerful methods for 3D scene reconstruction without camĀera calibration is based on Projective Geometry (Coxeter, 1994; Faugeras, 1992; Mohr and Arbogast, 1991) This method is very elegant and relies on the solution of a series of non-linear equations that allow the determination of the 3D coordinates of a point given the identity and 3D position of at least 6 reference points relying on two intersecting planes. The method is an ideal testbed for application of the variance propagation methodology for performance evaluation proposed by Haralick (1994).
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References
Coxeter, H.S.M. (1974) Projective Geometry,University of Toronto Press.
Faugeras, O.D. (1992) What can be seen in 3D with an uncalibrated stereo rig? Second European Conference on Computer Vision, Italy, 563-578.
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Haralick, R.M. (1994) Propagating covariance in computer vision, Proc. 12th Int. Conf. on Pattern Recognition,Jerusalem, 1:493-498.
Mohr, R. and Arbogast, E. (1991) It can be done without camera calibration, Pattern Recognition Letters, 12: 39 - 43.
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Ā© 2000 Springer Science+Business Media Dordrecht
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Petrou, M., Georgis, N., Kittler, J. (2000). Sensitivity Analysis of Projective Geometry 3D Reconstruction. In: Klette, R., Stiehl, H.S., Viergever, M.A., Vincken, K.L. (eds) Performance Characterization in Computer Vision. Computational Imaging and Vision, vol 17. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9538-4_20
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DOI: https://doi.org/10.1007/978-94-015-9538-4_20
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