  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).
