  The advancement of real-time 3D scene reconstruction and novel view synthesis
has been significantly propelled by 3D Gaussian Splatting (3DGS). However,
effectively training large-scale 3DGS and rendering it in real-time across
various scales remains challenging. This paper introduces CityGaussian
(CityGS), which employs a novel divide-and-conquer training approach and
Level-of-Detail (LoD) strategy for efficient large-scale 3DGS training and
rendering. Specifically, the global scene prior and adaptive training data
selection enables efficient training and seamless fusion. Based on fused
Gaussian primitives, we generate different detail levels through compression,
and realize fast rendering across various scales through the proposed
block-wise detail levels selection and aggregation strategy. Extensive
experimental results on large-scale scenes demonstrate that our approach
attains state-of-theart rendering quality, enabling consistent real-time
rendering of largescale scenes across vastly different scales. Our project page
is available at https://dekuliutesla.github.io/citygs/.
