In this paper, we propose a method to obtain a compact and accurate 3D wireframe representation from a single image by effectively exploiting global structural regularities. Our method trains a convolutional neural network to simultaneously detect salient junctions and straight lines, as well as predict their 3D depth and vanishing points. Compared with the state-of-the-art learning-based wireframe detection methods, our network is simpler and more unified, leading to better 2D wireframe detection. With global structural priors from parallelism, 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 on a large synthetic dataset of urban scenes as well as real images. Our code and datasets have been made public at https://github.com/zhou13/shapeunity.

翻译：在本文中,我们提出一种方法,通过有效利用全球结构规律,从单一图像中获取精确和精确的3D电线框架代表。我们的方法是训练一个革命性神经网络,同时探测突出的交界点和直线,并预测其3D深度和消失点。与最先进的基于学习的电线框架探测方法相比,我们的网络更简单、更统一,导致更好的2D电线框架探测。随着全球结构前科的平行,我们的方法进一步重建了完全的3D电线框架模型,一个适合诸如AR和CAD等各种高级视觉任务的紧凑矢量代表。我们广泛评价了庞大的城市景象合成数据集以及真实图像。我们的代码和数据集已经在https://github.com/13/shapunity上公布。