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
this https URL

本文提出了一种有效使用区域匹配的指导方针，并结合对称强度匹配的局部特征几何形状精炼方法，将不确定性传递到 RANSAC 中，并适应样本手性检查来进行单应性估计；实验结果表明，按照本文提出的指导方针，仿射求解器可以在更快的运行时间内实现与基于点的求解器同样的准确性。