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.
三维高斯喷溅（3DGS）的进步显著推动了实时三维场景重建和新颖视角合成的发展。然而，有效地训练大规模3DGS并在各种规模上实时渲染仍然具有挑战性。本文介绍了CityGaussian（CityGS），它采用了一种新颖的分而治之训练方法和细节级别（LoD）策略，以高效训练和渲染大规模3DGS。具体来说，全局场景先验和自适应训练数据选择使得训练高效且能无缝融合。基于融合的高斯原始体，我们通过压缩生成不同的细节级别，并通过提出的块状细节级别选择和聚合策略，实现在各种规模上的快速渲染。广泛的实验结果在大规模场景上展示了我们的方法达到了最先进的渲染质量，使得能够在极其不同的规模上实现大规模场景的一致实时渲染。