Interest Point Detectors Stability Evaluation on ApolloScape Dataset

  • Jacek KomorowskiEmail author
  • Konrad Czarnota
  • Tomasz Trzcinski
  • Lukasz Dabala
  • Simon Lynen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11133)


In 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.


Keypoint detectors Interest points stability 



This research was supported by Google Sponsor Research Agreement under the project “Efficient visual localization on mobile devices”.

The Titan X Pascal used for this research was donated by the NVIDIA Corporation.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Warsaw University of TechnologyWarsawPoland
  2. 2.TooplooxWrocławPoland
  3. 3.GoogleMountain ViewUSA

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