Learning to Reconstruct 3D Manhattan Wireframes from a Single Image

Abstract

From a single view of an urban environment, we propose a method to effectively exploit the global structural regularities for obtaining a compact, accurate, and intuitive 3D wireframe representation. Our method trains a single convolutional neural network to simultaneously detect salient junctions and straight lines, as well as predict their 3D depth and vanishing points. Compared with state-of-the-art learning-based wireframe detection methods, our network is much simpler and more unified, leading to better 2D wireframe detection. With a global structural prior (such as Manhattan assumption), our method further reconstructs a full 3D wireframe model, a compact vector representation suitable for a variety of high-level vision tasks such as AR and CAD. We conduct extensive evaluations of our method on a large new synthetic dataset of urban scenes as well as real images. Our code and datasets will be published along with the paper.