Title: Accurate Motion Estimation through Random Sample Aggregated Consensus,
Abstract: We reconsider the classic problem of estimating accurately a 2D
transformation from point matches between images containing outliers. RANSAC
discriminates outliers by randomly generating minimalistic sampled hypotheses
and verifying their consensus over the input data. Its response is based on the
single hypothesis that obtained the largest inlier support. In this article we
show that the resulting accuracy can be improved by aggregating all generated
hypotheses. This yields RANSAAC, a framework that improves systematically over
RANSAC and its state-of-the-art variants by statistically aggregating
hypotheses. To this end, we introduce a simple strategy that allows to rapidly
average 2D transformations, leading to an almost negligible extra computational
cost. We give practical applications on projective transforms and
homography+distortion models and demonstrate a significant performance gain in
both cases.