Fast and robust homography estimation by adaptive graduated non-convexity

Gaku Nakano, Takashi Shibata

Abstract: This paper proposes a novel fast and robust homography estimation by adaptively controlling the threshold of graduated non-convexity (GNC). Based on the fact that GNC is a variant of deterministic annealing, we provide a new method for updating the inlier threshold at each GNC iteration by utilizing the statistical properties of residuals of potential inliers. Contrary to RANSAC, our approach gives the same unique parameter for a single input due to without random sampling. Moreover, computational time increases linearly against outlier ratio changes, whereas RANSAC increases exponentially. Synthetic data evaluation shows that the proposed method is more robust and faster than RANSAC for highly contaminated data containing more than 80% outliers. Additionally, we demonstrate that our method works on severe real images that the state-of-the-art RANSAC method fails.