  Matching two images while estimating their relative geometry is a key step in
many computer vision applications. For decades, a well-established pipeline,
consisting of SIFT, RANSAC, and 8-point algorithm, has been used for this task.
Recently, many new approaches were proposed and shown to outperform previous
alternatives on standard benchmarks, including the learned features,
correspondence pruning algorithms, and robust estimators. However, whether it
is beneficial to incorporate them into the classic pipeline is
less-investigated. To this end, we are interested in i) evaluating the
performance of these recent algorithms in the context of image matching and
epipolar geometry estimation, and ii) leveraging them to design more practical
registration systems. The experiments are conducted in four large-scale
datasets using strictly defined evaluation metrics, and the promising results
provide insight into which algorithms suit which scenarios. According to this,
we propose three high-quality matching systems and a Coarse-to-Fine RANSAC
estimator. They show remarkable performances and have potentials to a large
part of computer vision tasks. To facilitate future research, the full
evaluation pipeline and the proposed methods are made publicly available.
