Making Affine Correspondences Work in Camera Geometry Computation
Local features such as SIFT and its affine and learned variants provide region-to-region rather than point-to-point correspondences. It has recently been exploited to create new minimal solvers for classical problems such as homography, essential and fundamental matrix estimation. The main argument in favor of such solvers is that their minimal sample size is smaller, e.g., only two instead of four matches are required to estimate a homography. Works proposing such solvers often claim a significant improvement in run-time thanks to fewer RANSAC iterations. We show that this argument is not valid in practice if the solvers are used naively as noise in the local feature geometries often causes RANSAC to fail to find an accurate solution. To overcome this, we propose guidelines for effective use of region-to-region matches in the course of a full model estimation pipeline. We propose a method for refining the local feature geometries by symmetric intensity-based matching, combine uncertainty propagation inside RANSAC with preemptive model verification, show a general scheme for computing uncertainty of minimal solvers results, and adapt the sample cheirality check for homography estimation to region-to-region correspondences. Our experiments show that affine solvers can achieve accuracy comparable to point-based solvers at faster run-times when following our guidelines. "