Reconstruction from Projections Using Grassmann Tensors
Abstract
In this paper a general method is given for reconstruction of a set of feature points in an arbitrary dimensional projective space from their projections into lower dimensional spaces. The method extends the methods applied in the well-studied problem of reconstruction of a set of scene points in \(\mathcal {P}^3\) given their projections in a set of images. In this case, the bifocal, trifocal and quadrifocal tensors are used to carry out this computation. It is shown that similar methods will apply in a much more general context, and hence may be applied to projections from \(\mathcal {P}^n\) to \(\mathcal {P}^m\), which have been used in the analysis of dynamic scenes. For sufficiently many generic projections, reconstruction of the scene is shown to be unique up to projectivity, except in the case of projections onto one-dimensional image spaces (lines).
Keywords
Projective Space Linear Subspace Image Space Diagonal Block Generic ProjectionReferences
- 1.Faugeras, O.D., Quan, L., Sturm, P.: Self-calibration of a 1D projective camera and its application to the self-calibration of a 2D projective camera. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1179–1185 (2000)CrossRefGoogle Scholar
- 2.Hartley, R.I.: Computation of the quadrifocal tensor. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 20–35. Springer, Heidelberg (1998)Google Scholar
- 3.Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
- 4.Heyden, A.: Reduced multilinear constraints: Theory and experiments. International Journal of Computer Vision 30(1), 5–26 (1998)CrossRefGoogle Scholar
- 5.Heyden, A.: Tensorial properties of multilinear constraints. Mathematical Methods in the Applied Sciences 23, 169–202 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
- 6.Quan, L.: Two-way ambiguity in 2d projective reconstruction from three uncalibrated 1d images. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2), 212–216 (2001)CrossRefGoogle Scholar
- 7.Quan, L., Kanade, T.: Affine structure from line correspondences with uncalibrated affine cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(8), 834–845 (1997)CrossRefGoogle Scholar
- 8.Semple, J.G., Kneebone, G.T.: Algebraic Projective Geometry. Oxford University Press, Oxford (1979)Google Scholar
- 9.Triggs, W.: The geometry of projective reconstruction i: Matching constraints and the joint image. Unpublished: Available on Bill Triggs’s web-site (1995)Google Scholar
- 10.Triggs, W.: Matching constraints and the joint image. In: Grimson, E. (ed.) Proc. 5th International Conference on Computer Vision, Boston, Cambridge, MA, June 1995, pp. 338–343 (1995)Google Scholar
- 11.Wolf, L., Shashua, A.: On projection matrices Pk → P2, k = 3,., 6, and their applications in computer vision. International Journal of Computer Vision 48(1), 53–67 (2002)zbMATHCrossRefGoogle Scholar