Abstract
M-estimator using iteratively reweighted least squares
(IRLS) is one of the best-known methods for robust estimation. However, IRLS is ineffective for robust unit-norm
constrained linear fitting (UCLF) problems, such as fundamental matrix estimation because of a poor initial solution.
We overcome this problem by developing a novel objective
function and its optimization, named iteratively reweighted
eigenvalues minimization (IREM). IREM is guaranteed to
decrease the objective function and achieves fast convergence and high robustness. In robust fundamental matrix estimation, IREM performs approximately 5-500 times
faster than random sampling consensus (RANSAC) while
preserving comparable or superior robustness