This paper introduces a method to generate a three-dimensional (3D) virtual model of an imaginary city from a single street-view image to represent the appearance of the city in a given input photograph. The proposed approach differs from reconstruction approaches, which generate a city model by guessing the city name from the input photograph. In contrast, we use machine learning to identify where to generate the city, what to allocate in the city, and how to arrange the components. We employ generative adversarial networks and convolutional neural networks to create a terrain map and identify the components and styles that represent the virtual city appearance. We demonstrate that our system creates 3D virtual cities that are visually similar in terms of plausibility and naturalness to actual cities corresponding to input photographs from around the world. To the best of our knowledge, this is the first work to generate a city model including all general city components, including streets, buildings, and vegetation, to match the style of a single input image.

中文翻译：

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CityCraft：从单个图像创建 3D 虚拟城市

本文介绍了一种从单个街景图像生成虚拟城市的三维 (3D) 虚拟模型的方法，以表示给定输入照片中城市的外观。所提出的方法不同于重建方法，后者通过从输入照片中猜测城市名称来生成城市模型。相比之下，我们使用机器学习来识别在哪里生成城市，在城市中分配什么，以及如何安排组件。我们采用生成对抗网络和卷积神经网络来创建地形图并识别代表虚拟城市外观的组件和样式。我们证明我们的系统创建了 3D 虚拟城市，这些城市在合理性和自然性方面与来自世界各地的输入照片对应的实际城市在视觉上相似。据我们所知，这是第一个生成包含所有一般城市组件（包括街道、建筑物和植被）以匹配单个输入图像风格的城市模型的工作。