Making Affine Correspondences Work in Camera Geometry Computation
This work addresses a practical bottleneck in computer vision for researchers and practitioners using local features, offering incremental improvements to existing methods.
The paper tackles the practical inefficiency of using affine correspondences for camera geometry estimation, showing that naive use of minimal solvers does not improve run-time as claimed. It proposes guidelines including feature refinement and uncertainty propagation, achieving comparable accuracy to point-based methods with faster run-times.
Local features e.g. SIFT and its affine and learned variants provide region-to-region rather than point-to-point correspondences. This has recently been exploited to create new minimal solvers for classical problems such as homography, essential and fundamental matrix estimation. The main advantage of such solvers is that their 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. 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. Our experiments show that affine solvers can achieve accuracy comparable to point-based solvers at faster run-times when following our guidelines. We make code available at https://github.com/danini/affine-correspondences-for-camera-geometry.