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Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection

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Pattern Recognition (GCPR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10496))

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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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Correspondence to Florian Kluger or Hanno Ackermann .

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Kluger, F., Ackermann, H., Yang, M.Y., Rosenhahn, B. (2017). Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection. In: Roth, V., Vetter, T. (eds) Pattern Recognition. GCPR 2017. Lecture Notes in Computer Science(), vol 10496. Springer, Cham. https://doi.org/10.1007/978-3-319-66709-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-66709-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66708-9

  • Online ISBN: 978-3-319-66709-6

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