1,522 research outputs found
Plane extraction for indoor place recognition
In this paper, we present an image based plane extraction
method well suited for real-time operations. Our approach exploits the
assumption that the surrounding scene is mainly composed by planes
disposed in known directions. Planes are detected from a single image
exploiting a voting scheme that takes into account the vanishing lines.
Then, candidate planes are validated and merged using a region grow-
ing based approach to detect in real-time planes inside an unknown in-
door environment. Using the related plane homographies is possible to
remove the perspective distortion, enabling standard place recognition
algorithms to work in an invariant point of view setup. Quantitative Ex-
periments performed with real world images show the effectiveness of our
approach compared with a very popular method
Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
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.Comment: Accepted for publication at German Conference on Pattern Recognition
(GCPR) 2017. This research was supported by German Research Foundation DFG
within Priority Research Programme 1894 "Volunteered Geographic Information:
Interpretation, Visualisation and Social Computing
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