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LIFT: Learned Invariant Feature Transform

  • Kwang Moo YiEmail author
  • Eduard Trulls
  • Vincent Lepetit
  • Pascal Fua
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9910)

Abstract

We introduce a novel Deep Network architecture that implements the full feature point handling pipeline, that is, detection, orientation estimation, and feature description. While previous works have successfully tackled each one of these problems individually, we show how to learn to do all three in a unified manner while preserving end-to-end differentiability. We then demonstrate that our Deep pipeline outperforms state-of-the-art methods on a number of benchmark datasets, without the need of retraining.

Keywords

Local features Feature descriptors Deep Learning 

Supplementary material

419981_1_En_28_MOESM1_ESM.zip (21.8 mb)
Supplementary material 1 (zip 22293 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Kwang Moo Yi
    • 1
    Email author
  • Eduard Trulls
    • 1
  • Vincent Lepetit
    • 2
  • Pascal Fua
    • 1
  1. 1.Computer Vision LaboratoryEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
  2. 2.Institute for Computer Graphics and VisionGraz University of TechnologyGrazAustria

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