Camera Resectioning from a Box
In this paper we describe how we can do camera resectioning from a box with unknown dimensions, i.e. determine the camera model, assuming that image pixels are square. This assumption is equivalent to assuming that the camera has an aspect ratio of one and zero skew, and this holds for most — if not all — digital cameras. Our proposed method works by first deriving 9 linear constraints on the projective camera matrix from the box, leaving a 3-dimensional subspace in which the projective camera matrix can lie. A single solution in this 3D subspace is then found via a method by Triggs in 1999, which uses the square pixel assumption to set up a 4th degree polynomial to which the solution is the desired model. This approach is, however, numerically challenging, and we use several means to tackle this issue. Lastly the solution is refined in an iterative manner, i.e. using bundle adjustment.
Unable to display preview. Download preview PDF.
- 2.Byröd, M., Josephson, K., Åström, K.: Improving numerical accuracy of gröbner basis polynomial equation solvers. In: International Conference on Computer Vision (2007)Google Scholar
- 3.Byröd, M., Josephson, K., Åström, K.: A column-pivoting based strategy for monomial ordering in numerical gröbner basis calculations. In: The 10th European Conference on Computer Vision (2008)Google Scholar
- 4.Byröd, M., Kukelova, Z., Josephson, K., Pajdla, T., Åström, K.: Fast and robust numerical solutions to minimal problems for cameras with radial distortion. In: Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
- 10.Kukelova, M., Bujnak, Z., Pajdla, T.: Automatic generator of minimal problem solvers. In: The 10th European Conference on Computer Vision, pp. 302–315 (2008)Google Scholar
- 12.Seitz, S.M., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 519–528 (2006)Google Scholar
- 15.Stewénius, H., Schaffalitzky, F., Nistér, D.: How hard is three-view triangulation really? In: Proc. Int. Conf. on Computer Vision, Beijing, China, pp. 686–693 (2005)Google Scholar
- 16.Triggs, B.: Camera pose and calibration from 4 or 5 known 3D points. In: Proc. 7th Int. Conf. on Computer Vision, pp. 278–284. IEEE Computer Society Press, Los Alamitos (1999)Google Scholar