Interest Point Detectors Stability Evaluation on ApolloScape Dataset
Jacek Komorowski , Konrad Czarnota , Tomasz Trzciński , Łukasz Dąbała , Simon Lynen
AbstractIn the recent years, a number of novel, deep-learning based, interest point detectors, such as LIFT, DELF, Superpoint or LF-Net was proposed. However there’s a lack of a standard benchmark to evaluate suitability of these novel keypoint detectors for real-live applications such as autonomous driving. Traditional benchmarks (e.g. Oxford VGG) are rather limited, as they consist of relatively few images of mostly planar scenes taken in favourable conditions. In this paper we verify if the recent, deep-learning based interest point detectors have the advantage over the traditional, hand-crafted keypoint detectors. To this end, we evaluate stability of a number of hand crafted and recent, learning-based interest point detectors on the street-level view ApolloScape dataset.
|Publication size in sheets||0.6|
Leal-Taixé Laura, Roth Stefan (eds.): Computer Vision – ECCV 2018 Workshops, Lecture Notes In Computer Science, vol. 11133, 2019, Springer, : Gewerbestrasse 11, 6330 Cham, Switzerland, Springer, ISBN 978-3-030-11020-8, [978-3-030-11021-5 (eBook)], 753 p.
2019_Bookmatter_ComputerVisionECCV2018Workshop.pdf / No licence information (file archived - login or check accessibility on faculty)
|Keywords in English||Keypoint detectors Interest points stability|
|Score||= 140.0, 02-02-2020, ChapterFromConference|
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