In recent years, advances in computer hardware, graphics rendering algorithms and computer vision have enabled the utilization of 3D building reconstructions in the fields of archeological structure restoration and urban planning. This paper deals with the reconstruction of realistic 3D models of buildings façades, in the urban environment for cultural heritage. The proposed approach is an extension of our previous work in this research topic, which introduced a methodology for accurate 3D realistic façade reconstruction by defining and exploiting a relation between stereoscopic image and tacheometry data. In this work, we re-purpose well known deep neural network architectures in the fields of image segmentation and single image depth prediction, for the tasks of façade structural element detection, depth point-cloud generation and protrusion estimation, with the goal of alleviating drawbacks in our previous design, resulting in a more light-weight, robust, flexible and cost-effective design.

中文翻译：

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使用深度学习进行3D建筑立面重建

近年来，计算机硬件，图形渲染算法和计算机视觉的进步已使3D建筑重建可用于考古结构修复和城市规划领域。本文涉及在文化遗产的城市环境中重建建筑立面的逼真的3D模型。拟议的方法是我们先前在该研究主题中的工作的扩展，后者通过定义和利用立体图像与测速数据之间的关系，引入了一种用于精确3D逼真的立面重建的方法。在这项工作中，我们将著名的深度神经网络架构重新用于图像分割和单个图像深度预测领域，以进行立面结构元素检测，