Accurate Motion Estimation through Random Sample Aggregated Consensus
This work addresses a classic computer vision problem for image processing and robotics, offering an incremental improvement over existing RANSAC variants.
The paper tackles the problem of accurately estimating 2D transformations from point matches with outliers, and shows that aggregating all generated hypotheses in RANSAC improves accuracy, leading to significant performance gains in applications like projective transforms and homography+distortion models.
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.