  The horizon line is an important contextual attribute for a wide variety of
image understanding tasks. As such, many methods have been proposed to estimate
its location from a single image. These methods typically require the image to
contain specific cues, such as vanishing points, coplanar circles, and regular
textures, thus limiting their real-world applicability. We introduce a large,
realistic evaluation dataset, Horizon Lines in the Wild (HLW), containing
natural images with labeled horizon lines. Using this dataset, we investigate
the application of convolutional neural networks for directly estimating the
horizon line, without requiring any explicit geometric constraints or other
special cues. An extensive evaluation shows that using our CNNs, either in
isolation or in conjunction with a previous geometric approach, we achieve
state-of-the-art results on the challenging HLW dataset and two existing
benchmark datasets.
