TY - CONF
T1 - A Scalable Projective Bundle Adjustment Algorithm using the L infinity Norm
T2 - Computer Vision, Graphics Image Processing, 2008. ICVGIP '08. Sixth Indian Conference on
Y1 - 2008
A1 - Mitra, K.
A1 - Chellapa, Rama
KW - adjustment
KW - algorithm;structure
KW - bundle
KW - complexity;convex
KW - complexity;iteration
KW - error;scalable
KW - estimation;
KW - estimation;cameras;computational
KW - L_{infin}
KW - method;large
KW - methods;parameter
KW - norm;camera;computational
KW - OPTIMIZATION
KW - parameter
KW - problem;memory
KW - problem;projection
KW - problem;reprojection
KW - programming;image
KW - projective
KW - reconstruction;iterative
KW - reconstruction;quasiconvex
KW - requirement;motion
KW - scale
AB - The traditional bundle adjustment algorithm for structure from motion problem has a computational complexity of O((m+n)^{3}) per iteration and memory requirement of O(mn(m+n)), where m is the number of cameras and n is the number of structure points. The sparse version of bundle adjustment has a computational complexity of O(m^{3}+mn) per iteration and memory requirement of O(mn). Here we propose an algorithm that has a computational complexity of O(mn(radicm+radicn)) per iteration and memory requirement of O(max(m,n)). The proposed algorithm is based on minimizing the L_{infin} norm of reprojection error. It alternately estimates the camera and structure parameters, thus reducing the potentially large scale optimization problem to many small scale subproblems each of which is a quasi-convex optimization problem and hence can be solved globally. Experiments using synthetic and real data show that the proposed algorithm gives good performance in terms of minimizing the reprojection error and also has a good convergence rate.
JA - Computer Vision, Graphics Image Processing, 2008. ICVGIP '08. Sixth Indian Conference on
M3 - 10.1109/ICVGIP.2008.51
ER -