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2017 | Supplement | Buchkapitel

Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection

verfasst von : Florian Kluger, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn

Erschienen in: Pattern Recognition

Verlag: Springer International Publishing

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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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Titel: Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
verfasst von: Florian Kluger
Hanno Ackermann
Michael Ying Yang
Bodo Rosenhahn
Verlag: Springer International Publishing
Buch: Pattern Recognition
Print ISBN: 978-3-319-66708-9

Electronic ISBN: 978-3-319-66709-6

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-66709-6_2

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