UrbanGS: A Scalable and Efficient Architecture for Geometrically Accurate Large-Scene Reconstruction

• URL: http://arxiv.org/abs/2602.02089v1
• Date: Mon, 02 Feb 2026 13:35:16 GMT
• Title: UrbanGS: A Scalable and Efficient Architecture for Geometrically Accurate Large-Scene Reconstruction
• Authors: Changbai Li, Haodong Zhu, Hanlin Chen, Xiuping Liang, Tongfei Chen, Shuwei Shao, Linlin Yang, Huobin Tan, Baochang Zhang,
• Abstract summary: UrbanGS is a scalable reconstruction framework for city-scale applications.<n>It addresses issues of geometric consistency, memory efficiency, and computational scalability.<n>Experiments on multiple urban datasets demonstrate that UrbanGS achieves superior performance in rendering quality, geometric accuracy, and memory efficiency.
• Score: 30.233380858586454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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