Recently, 3D Gaussian Splatting and its derivatives have achieved significant breakthroughs in large-scale scene reconstruction. However, how to efficiently and stably achieve high-quality geometric fidelity remains a core challenge. To address this issue, we introduce MetroGS, a novel Gaussian Splatting framework for efficient and robust reconstruction in complex urban environments. Our method is built upon a distributed 2D Gaussian Splatting representation as the core foundation, serving as a unified backbone for subsequent modules. To handle potential sparse regions in complex scenes, we propose a structured dense enhancement scheme that utilizes SfM priors and a pointmap model to achieve a denser initialization, while incorporating a sparsity compensation mechanism to improve reconstruction completeness. Furthermore, we design a progressive hybrid geometric optimization strategy that organically integrates monocular and multi-view optimization to achieve efficient and accurate geometric refinement. Finally, to address the appearance inconsistency commonly observed in large-scale scenes, we introduce a depth-guided appearance modeling approach that learns spatial features with 3D consistency, facilitating effective decoupling between geometry and appearance and further enhancing reconstruction stability. Experiments on large-scale urban datasets demonstrate that MetroGS achieves superior geometric accuracy, rendering quality, offering a unified solution for high-fidelity large-scale scene reconstruction.

翻译：近期，3D高斯泼溅（3D Gaussian Splatting）及其衍生方法在大规模场景重建中取得了显著突破。然而，如何高效且稳定地实现高几何保真度仍是核心难题。为解决该问题，我们提出MetroGS——一种用于复杂城市环境高效鲁棒重建的新型高斯泼溅框架。该方法以分布式2D高斯泼溅表示为底层核心，作为后续模块的统一骨干。针对复杂场景中可能出现的稀疏区域，我们提出结构化密集增强方案，利用SfM先验和点图模型实现更密集的初始化，并引入稀疏性补偿机制以提升重建完整性。此外，我们设计渐进式混合几何优化策略，有机融合单目与多视角优化，实现高效精准的几何精化。最后，针对大规模场景中普遍存在的外观不一致性问题，我们引入深度引导的外观建模方法，学习具备三维一致性的空间特征，促进几何与外观的有效解耦，进一步提升重建稳定性。在大规模城市数据集上的实验表明，MetroGS在几何精度、渲染质量方面均表现卓越，为高保真大规模场景重建提供了统一解决方案。