  Feature matching is one of the most fundamental and active research areas in
computer vision. A comprehensive evaluation of feature matchers is necessary,
since it would advance both the development of this field and also high-level
applications such as Structure-from-Motion or Visual SLAM. However, to the best
of our knowledge, no previous work targets the evaluation of feature matchers
while they only focus on evaluating feature detectors and descriptors. This
leads to a critical absence in this field that there is no standard datasets
and evaluation metrics to evaluate different feature matchers fairly. To this
end, we present the first uniform feature matching benchmark to facilitate the
evaluation of feature matchers. In the proposed benchmark, matchers are
evaluated in different aspects, involving matching ability, correspondence
sufficiency, and efficiency. Also, their performances are investigated in
different scenes and in different matching types. Subsequently, we carry out an
extensive evaluation of different state-of-the-art matchers on the benchmark
and make in-depth analyses based on the reported results. This can be used to
design practical matching systems in real applications and also advocates the
potential future research directions in the field of feature matching.
