RLSCNet: A Residual Line-Shaped Convolutional Network for Vanishing Point Detection

Abstract

The convolutional neural network (CNN) is an effective model for vanishing point (VP) detection, but its success heavily relies on a massive amount of training data to ensure high accuracy. Without sufficient and balanced training data, the obtained CNN-based VP detection models can be easily overfitted with less generalization. By acknowledging that a VP in the image is the intersection of projections of multiple parallel lines in the scene and treating this knowledge as a geometric prior, we propose a prior-guided residual line-shaped convolutional network for VP detection to reduce the dependence of CNN on training data. In the proposed end-to-end approach, the probabilities of VP in the image are computed through an edge extraction subnetwork and a VP prediction subnetwork, which explicitly establishes the geometric relationships among edges, lines, and vanishing points by stacking the differentiable residual line-shaped convolutional modules. Our extensive experiments on various datasets show that the proposed VP detection network improves accuracy and outperforms previous methods in terms of both inference speed and generalization performance.