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As an important part of linear perspective, vanishing points (VPs) provideuseful clues for mapping objects from 2D photos to 3D space. Existing methodsare mainly focused on extracting structural features such as lines or contoursand then clustering these features to detect VPs. However, these techniquessuffer from ambiguous information due to the large number of line segments andcontours detected in outdoor environments. In this paper, we present a newconvolutional neural network (CNN) to detect dominant VPs in natural scenes,i.e., the Dominant Vanishing Point detection Network (D-VPnet). The keycomponent of our method is the feature line-segment proposal unit (FLPU), whichcan be directly utilized to predict the location of the dominant VP. Moreover,the model also uses the two main parallel lines as an assistant to determinethe position of the dominant VP. The proposed method was tested using a publicdataset and a Parallel Line based Vanishing Point (PLVP) dataset. Theexperimental results suggest that the detection accuracy of our approachoutperforms those of state-of-the-art methods under various conditions inreal-time, achieving rates of 115fps.

D-VPnet: A Network for Real-time Dominant Vanishing Point Detection in Natural Scenes