While 3D Gaussian Splatting (3DGS) enables high-quality, real-time rendering for bounded scenes, its extension to large-scale urban environments gives rise to critical challenges in terms of geometric consistency, memory efficiency, and computational scalability. To address these issues, we present UrbanGS, a scalable reconstruction framework that effectively tackles these challenges for city-scale applications. First, we propose a Depth-Consistent D-Normal Regularization module. Unlike existing approaches that rely solely on monocular normal estimators, which can effectively update rotation parameters yet struggle to update position parameters, our method integrates D-Normal constraints with external depth supervision. This allows for comprehensive updates of all geometric parameters. By further incorporating an adaptive confidence weighting mechanism based on gradient consistency and inverse depth deviation, our approach significantly enhances multi-view depth alignment and geometric coherence, which effectively resolves the issue of geometric accuracy in complex large-scale scenes. To improve scalability, we introduce a Spatially Adaptive Gaussian Pruning (SAGP) strategy, which dynamically adjusts Gaussian density based on local geometric complexity and visibility to reduce redundancy. Additionally, a unified partitioning and view assignment scheme is designed to eliminate boundary artifacts and optimize computational load. Extensive experiments on multiple urban datasets demonstrate that UrbanGS achieves superior performance in rendering quality, geometric accuracy, and memory efficiency, providing a systematic solution for high-fidelity large-scale scene reconstruction.

翻译：尽管3D高斯溅射（3DGS）能够为有界场景提供高质量实时渲染，但其扩展到大规模城市环境时，在几何一致性、内存效率和计算可扩展性方面引发了关键挑战。为解决这些问题，我们提出了UrbanGS，一个可扩展的重建框架，能有效应对城市级应用中的这些挑战。首先，我们提出了深度一致性D-法向正则化模块。与现有仅依赖单目法向估计器的方法不同——这类方法虽能有效更新旋转参数却难以更新位置参数——我们的方法将D-法向约束与外部深度监督相结合，从而实现对所有几何参数的全面更新。通过进一步引入基于梯度一致性和逆深度偏差的自适应置信加权机制，我们的方法显著增强了多视角深度对齐与几何连贯性，有效解决了复杂大规模场景中的几何精度问题。为提升可扩展性，我们提出了空间自适应高斯剪枝（SAGP）策略，该策略根据局部几何复杂度和可见性动态调整高斯密度以降低冗余。此外，设计了统一分区与视角分配方案，以消除边界伪影并优化计算负载。在多个城市数据集上的大量实验表明，UrbanGS在渲染质量、几何精度和内存效率方面均取得优越性能，为高保真大规模场景重建提供了系统化解决方案。