Towards the Application of Linear Programming Methods For Multi-Camera Pose Estimation
This is an incremental improvement for computer vision researchers working on camera calibration and 3D reconstruction.
The paper tackles multi-camera pose estimation by introducing a separation-based optimization algorithm that avoids matrix inversion, using nonlinear functions and convex quadratic polynomials to minimize reprojection error.
We presented a separation based optimization algorithm which, rather than optimization the entire variables altogether, This would allow us to employ: 1) a class of nonlinear functions with three variables and 2) a convex quadratic multivariable polynomial, for minimization of reprojection error. Neglecting the inversion required to minimize the nonlinear functions, in this paper we demonstrate how separation allows eradication of matrix inversion.