The purpose of this study is to give a performance comparison between several
classic hand-crafted and deep key-point detector and descriptor methods. In
particular, we consider the following classical algorithms: SIFT, SURF, ORB,
FAST, BRISK, MSER, HARRIS, KAZE, AKAZE, AGAST, GFTT, FREAK, BRIEF and RootSIFT,
where a subset of all combinations is paired into detector-descriptor
pipelines. Additionally, we analyze the performance of two recent and
perspective deep detector-descriptor models, LF-Net and SuperPoint. Our
benchmark relies on the HPSequences dataset that provides real and diverse
images under various geometric and illumination changes. We analyze the
performance on three evaluation tasks: keypoint verification, image matching
and keypoint retrieval. The results show that certain classic and deep
approaches are still comparable, with some classic detector-descriptor
combinations overperforming pretrained deep models. In terms of the execution
times of tested implementations, SuperPoint model is the fastest, followed by
ORB. The source code is published on
\url{https://github.com/kristijanbartol/keypoint-algorithms-benchmark}