  A new algorithm is proposed to accelerate RANSAC model quality calculations.
The method is based on partitioning the joint correspondence space, e.g., 2D-2D
point correspondences, into a pair of regular grids. The grid cells are mapped
by minimal sample models, estimated within RANSAC, to reject correspondences
that are inconsistent with the model parameters early. The proposed technique
is general. It works with arbitrary transformations even if a point is mapped
to a point set, e.g., as a fundamental matrix maps to epipolar lines. The
method is tested on thousands of image pairs from publicly available datasets
on fundamental and essential matrix, homography and radially distorted
homography estimation. On average, it reduces the RANSAC run-time by 41% with
provably no deterioration in the accuracy. It can be straightforwardly plugged
into state-of-the-art RANSAC frameworks, e.g. VSAC.
