Rank-constrained estimation of the absolute quadric
This page contains code/data/results for an autocalibration method that
directly upgrades from a projective to metric reconstruction by estimating
the dual absolute quadric. Main features of the algorithm are:
- Enforces theoretical requirements such as positive semidefiniteness
and rank degeneracy while estimating the absolute quadric.
- Achieves the globally optimal solution for a reasonable objective
function by constructing a series of convex LMI relaxations.
- Complexity independent of number of views.
VRML reconstructions for various datasets can be found
The zip file contains a README.pdf that details the contents and
M.K. Chandraker, S. Agarwal, F. Kahl, D. Nistér and D.J. Kriegman
Autocalibration via Rank-Constrained Estimation of the Absolute Quadric
CVPR 2007, Minneapolis, Minnesota.
Last updated June 27, 2007.