RealCity3D: A Large-scale Georeferenced 3D Shape Dataset of Real-world Cities

Congcong Wen, Wenyu Han, Lazarus Chok, Yan Liang Tan, Sheung Lung Chan, Hang Zhao, Chen Feng

Keywords: 3D city, content generation, city generation, real world

TL;DR: Data-driven city generation is challenging even with one million geo-referenced 3D building models

Abstract: Existing 3D shape datasets in the research community are generally limited to objects or scenes at the home level. City-level shape datasets are rare due to the difficulty in data collection and processing. However, such datasets uniquely present a new type of 3D data with a high variance in geometric complexity and spatial layout styles, such as residential/historical/commercial buildings and skyscrapers. This work focuses on collecting such data, and proposes city generation as new tasks for data-driven content generation. Thus, we collect over 1,000,000 geo-referenced 3D building models from New York City and Zurich. We benchmark various baseline performances on two challenging tasks: (1) city layout generation, and (2) building shape generation. Moreover, we propose an auto-encoding tree neural network for 2D building footprint and 3D building cuboid generation. The dataset, tools, and algorithms will be released to the community.

URL: https://github.com/ai4ce/RealCity3D