Interest point detectors stability evaluation on ApolloScape dataset

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.

[1]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[2]  Vincent Lepetit,et al.  TILDE: A Temporally Invariant Learned DEtector , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Paul Newman,et al.  1 year, 1000 km: The Oxford RobotCar dataset , 2017, Int. J. Robotics Res..

[4]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[5]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[6]  Darius Burschka,et al.  Adaptive and Generic Corner Detection Based on the Accelerated Segment Test , 2010, ECCV.

[7]  Michael Bosse,et al.  Get Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization , 2015, Robotics: Science and Systems.

[8]  Andrea Vedaldi,et al.  HPatches: A Benchmark and Evaluation of Handcrafted and Learned Local Descriptors , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Serge J. Belongie,et al.  Learning to Detect and Match Keypoints with Deep Architectures , 2016, BMVC.

[10]  Tomasz Malisiewicz,et al.  SuperPoint: Self-Supervised Interest Point Detection and Description , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[11]  Adrien Bartoli,et al.  Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces , 2013, BMVC.

[12]  Ruigang Yang,et al.  The ApolloScape Open Dataset for Autonomous Driving and Its Application , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Matthew A. Brown,et al.  Automatic Panoramic Image Stitching using Invariant Features , 2007, International Journal of Computer Vision.

[14]  Cordelia Schmid,et al.  Comparing and evaluating interest points , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[15]  Pascal Fua,et al.  Training for Task Specific Keypoint Detection , 2009, DAGM-Symposium.

[16]  Jiri Matas,et al.  In the Saddle: Chasing fast and repeatable features , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[17]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[18]  Vincent Lepetit,et al.  BRIEF: Computing a Local Binary Descriptor Very Fast , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Ruigang Yang,et al.  The ApolloScape Dataset for Autonomous Driving , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[20]  Richard Szeliski,et al.  Building Rome in a day , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[21]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[22]  Vincent Lepetit,et al.  LIFT: Learned Invariant Feature Transform , 2016, ECCV.

[23]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[24]  Pascal Fua,et al.  LF-Net: Learning Local Features from Images , 2018, NeurIPS.

[25]  Trevor Darrell,et al.  BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling , 2018, ArXiv.

[26]  Torsten Sattler,et al.  Quad-Networks: Unsupervised Learning to Rank for Interest Point Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[28]  Bohyung Han,et al.  Large-Scale Image Retrieval with Attentive Deep Local Features , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).