  While RANSAC-based methods are robust to incorrect image correspondences
(outliers), their hypothesis generators are not robust to correct image
correspondences (inliers) with positional error (noise). This slows down their
convergence because hypotheses drawn from a minimal set of noisy inliers can
deviate significantly from the optimal model. This work addresses this problem
by introducing ANSAC, a RANSAC-based estimator that accounts for noise by
adaptively using more than the minimal number of correspondences required to
generate a hypothesis. ANSAC estimates the inlier ratio (the fraction of
correct correspondences) of several ranked subsets of candidate correspondences
and generates hypotheses from them. Its hypothesis-generation mechanism
prioritizes the use of subsets with high inlier ratio to generate high-quality
hypotheses. ANSAC uses an early termination criterion that keeps track of the
inlier ratio history and terminates when it has not changed significantly for a
period of time. The experiments show that ANSAC finds good homography and
fundamental matrix estimates in a few iterations, consistently outperforming
state-of-the-art methods.
