DeepVP: Deep Learning for Vanishing Point Detection on 1 Million Street View Images

Abstract

We propose a novel approach to detect vanishing points in images using a convolutional neural network (CNN) trained on a newly collected Google street-view image dataset. By utilizing the camera parameters and road direction data from Google street view, we collected a total of 1,053,425 images with inferred ground-truth vanishing points, along 23 worldwide routes totaling 125,165 kilometers. We then formulate vanishing point detection as a CNN classification problem using an output layer with 225 discrete possible vanishing point locations. Experimental results show that our deep vanishing point system outperforms the state-of-the-art algorithmic vanishing point detector. We achieved 99% accuracy in recovering the horizon line and 92% in locating the vanishing point within a ±5-degree range.