Deterministic Fitting of Multiple Structures Using Iterative MaxFS with Inlier Scale Estimation

Abstract

We present an efficient deterministic hypothesis generation algorithm for robust fitting of multiple structures based on the maximum feasible subsystem (MaxFS) framework. Despite its advantage, a global optimization method such as MaxFS has two main limitations for geometric model fitting. First, its performance is much influenced by the user-specified inlier scale. Second, it is computationally inefficient for large data. The presented algorithm, called iterative MaxFS with inlier scale (IMaxFS-ISE), iteratively estimates model parameters and inlier scale and also overcomes the second limitation by reducing data for the MaxFS problem. The IMaxFS-ISE algorithm generates hypotheses only with top-n ranked subsets based on matching scores and data fitting residuals. This reduction of data for the MaxFS problem makes the algorithm computationally realistic. A sequential "fitting and remaving" procedure is repeated until overall energy function does not decrease. Experimental results demonstrate that our method can generate more reliable and consistent hypotheses than random sampling-based methods for estimating multiple structures from data with many outliers.