3D city generation with NeRF-based methods shows promising generation results
but is computationally inefficient. Recently 3D Gaussian Splatting (3D-GS) has
emerged as a highly efficient alternative for object-level 3D generation.
However, adapting 3D-GS from finite-scale 3D objects and humans to
infinite-scale 3D cities is non-trivial. Unbounded 3D city generation entails
significant storage overhead (out-of-memory issues), arising from the need to
expand points to billions, often demanding hundreds of Gigabytes of VRAM for a
city scene spanning 10km^2. In this paper, we propose GaussianCity, a
generative Gaussian Splatting framework dedicated to efficiently synthesizing
unbounded 3D cities with a single feed-forward pass. Our key insights are
two-fold: 1) Compact 3D Scene Representation: We introduce BEV-Point as a
highly compact intermediate representation, ensuring that the growth in VRAM
usage for unbounded scenes remains constant, thus enabling unbounded city
generation. 2) Spatial-aware Gaussian Attribute Decoder: We present
spatial-aware BEV-Point decoder to produce 3D Gaussian attributes, which
leverages Point Serializer to integrate the structural and contextual
characteristics of BEV points. Extensive experiments demonstrate that
GaussianCity achieves state-of-the-art results in both drone-view and
street-view 3D city generation. Notably, compared to CityDreamer, GaussianCity
exhibits superior performance with a speedup of 60 times (10.72 FPS v.s. 0.18
FPS).

高效合成无边界 3D 城市的 GaussianCity 系统，采用 3D 城市生成的 NeRF 和 3D-Gauusian Splatting 方法，通过引入 BEV-Point 作为紧凑的中间表示和空间感知的 BEV-Point 解码器，实现了优于 CityDreamer 的最先进的无人机视角和街景视角下的 3D 城市生成性能提升 60 倍（10.72 FPS 对 0.18 FPS）。

GaussianCity：基于高斯点绘制的无界三维城市生成

GaussianCity: Generative Gaussian Splatting for Unbounded 3D City  Generation

In recent years, extensive research has focused on 3D natural scene
generation, but the domain of 3D city generation has not received as much
exploration. This is due to the greater challenges posed by 3D city generation,
mainly because humans are more sensitive to structural distortions in urban
environments. Additionally, generating 3D cities is more complex than 3D
natural scenes since buildings, as objects of the same class, exhibit a wider
range of appearances compared to the relatively consistent appearance of
objects like trees in natural scenes. To address these challenges, we propose
CityDreamer, a compositional generative model designed specifically for
unbounded 3D cities, which separates the generation of building instances from
other background objects, such as roads, green lands, and water areas, into
distinct modules. Furthermore, we construct two datasets, OSM and GoogleEarth,
containing a vast amount of real-world city imagery to enhance the realism of
the generated 3D cities both in their layouts and appearances. Through
extensive experiments, CityDreamer has proven its superiority over
state-of-the-art methods in generating a wide range of lifelike 3D cities.

提出了 CityDreamer，一种针对无限制 3D 城市的组合生成模型，通过将建筑实例的生成与其他背景对象（如道路、绿地和水域）分离为不同模块来解决生成 3D 城市时面临的挑战，并构建了 OSM 和 GoogleEarth 两个数据集以增强所生成的 3D 城市的布局和外观的真实性，通过广泛的实验证明了 CityDreamer 在生成各种逼真的 3D 城市方面的优越性。