# HarrisZ$^+$: Harris Corner Selection for Next-Gen Image Matching Pipelines Source: [HarrisZ$^+$: Harris Corner Selection for Next-Gen Image Matching Pipelines]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:33:17.458Z Authors: Fabio Bellavia, Dmytro Mishkin Published: 2021-09-27T10:21:03.000Z ## Abstract Due to its role in many computer vision tasks, image matching has been subjected to an active investigation by researchers, which has lead to better and more discriminant feature descriptors and to more robust matching strategies, also thanks to the advent of the deep learning and the increased computational power of the modern hardware. Despite of these achievements, the keypoint extraction process at the base of the image matching pipeline has not seen equivalent progresses. This paper presents HarrisZ$^+$, an upgrade to the HarrisZ corner detector, optimized to synergically take advance of the recent improvements of the other steps of the image matching pipeline. HarrisZ$^+$ does not only consists of a tuning of the setup parameters, but introduces further refinements to the selection criteria delineated by HarrisZ, so providing more, yet discriminative, keypoints, which are better distributed on the image and with higher localization accuracy. The image matching pipeline including HarrisZ$^+$, together with the other modern components, obtained in different recent matching benchmarks state-of-the-art results among the classic image matching pipelines. These results are quite close to those obtained by the more recent fully deep end-to-end trainable approaches and show that there is still a proper margin of improvement that can be granted by the research in classic image matching methods. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Stereo Matching on IMC Phototourism 2021](): Stereo Matching; IMC Phototourism 2021; mAA@10°: 76.06. - [Stereo Matching on IMC PragueParks 2021](): Stereo Matching; IMC PragueParks 2021; mAA @ 10°: 74.23. - [Stereo Matching on IMC GoogleUrban 2021](): Stereo Matching; IMC GoogleUrban 2021; mAA@10°: 35.67. - [Stereo Matching on IMC Total 2021](): Stereo Matching; IMC Total 2021; mAA@10°: 55.04. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()