GaussianCity: Generative Gaussian Splatting for Unbounded 3D City Generation
<|reference_start|>GaussianCity: Generative Gaussian Splatting for Unbounded 3D City Generation: 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).<|reference_end|>
@article{xie2024gaussiancity:,
    title={GaussianCity: Generative Gaussian Splatting for Unbounded 3D City
  Generation},
    author={Haozhe Xie, Zhaoxi Chen, Fangzhou Hong, Ziwei Liu},
    journal={arXiv preprint arXiv:2406.06526},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06526},
    primaryClass={cs.CV}
}