ANSAC: Adaptive Non-minimal Sample and Consensus

TL;DR

ANSAC introduces an adaptive approach that improves robustness and speed in estimating models from noisy data by prioritizing high inlier ratio subsets and employing early termination.

Contribution

It proposes ANSAC, a novel RANSAC-based estimator that adaptively uses more correspondences and estimates inlier ratios to enhance convergence and accuracy.

Findings

Outperforms state-of-the-art methods in homography estimation

Converges faster with fewer iterations

Effectively handles noisy correspondences

Abstract

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.