Recently, 3D Gaussian Splatting (3DGS) has revolutionized radiance field reconstruction, manifesting efficient and high-fidelity novel view synthesis. However, accurately representing surfaces, especially in large and complex scenarios, remains a significant challenge due to the unstructured nature of 3DGS. In this paper, we present CityGaussianV2, a novel approach for large-scale scene reconstruction that addresses critical challenges related to geometric accuracy and efficiency. Building on the favorable generalization capabilities of 2D Gaussian Splatting (2DGS), we address its convergence and scalability issues. Specifically, we implement a decomposed-gradient-based densification and depth regression technique to eliminate blurry artifacts and accelerate convergence. To scale up, we introduce an elongation filter that mitigates Gaussian count explosion caused by 2DGS degeneration. Furthermore, we optimize the CityGaussian pipeline for parallel training, achieving up to 10× compression, at least 25% savings in training time, and a 50% decrease in memory usage. We also established standard geometry benchmarks under large-scale scenes. Experimental results demonstrate that our method strikes a promising balance between visual quality, geometric accuracy, as well as storage and training costs.
近年来，3D Gaussian Splatting（3DGS）革新了辐射场重建，展现了高效且高保真的新视图合成能力。然而，由于3DGS的非结构化特性，准确表示表面，特别是在大规模和复杂场景中，仍然是一个显著的挑战。本文提出了一种新的大规模场景重建方法——CityGaussianV2，专注于解决几何精度和效率相关的关键问题。在2D Gaussian Splatting（2DGS）良好泛化能力的基础上，我们解决了其收敛和可扩展性问题。具体而言，我们实现了基于分解梯度的密化和深度回归技术，以消除模糊伪影并加速收敛。为实现扩展，我们引入了一种拉伸滤波器，以缓解由2DGS退化引起的高斯数量膨胀。此外，我们优化了CityGaussian的训练管道以支持并行训练，达到了最高10倍的压缩效果，至少节省25%的训练时间，并减少50%的内存使用。我们还在大规模场景下建立了标准几何基准测试。实验结果表明，我们的方法在视觉质量、几何精度、存储和训练成本之间实现了良好的平衡。