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Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection

  • Florian Kluger
  • Hanno Ackermann
  • Michael Ying Yang
  • Bodo Rosenhahn
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10496)

Abstract

We present a novel approach for vanishing point detection from uncalibrated monocular images. In contrast to state-of-the-art, we make no a priori assumptions about the observed scene. Our method is based on a convolutional neural network (CNN) which does not use natural images, but a Gaussian sphere representation arising from an inverse gnomonic projection of lines detected in an image. This allows us to rely on synthetic data for training, eliminating the need for labelled images. Our method achieves competitive performance on three horizon estimation benchmark datasets. We further highlight some additional use cases for which our vanishing point detection algorithm can be used.

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Leibniz Universität HannoverHanoverGermany
  2. 2.University of TwenteEnschedeThe Netherlands

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