Evaluating 3D Gaussian Splatting for Urban Scene Reconstruction

Accurate, detailed and efficient 3D reconstructions of large-scale urban environments are essential for applications such as autonomous driving, urban planning and digital twin construction. Recent advances in 3D Gaussian Splatting (3DGS) have shown remarkable potential in photorealistic novel view synthesis and high-fidelity scene reconstruction, but their applicability to large-scale urban reconstruction remains underexplored and often challenging. In this work, we present a comprehensive evaluation of 3D Gaussian Splatting techniques applied to urban scale 3D reconstruction. We systematically benchmark GS-based methods on diverse urban datasets, analyzing their performance in terms of scalability, geometric accuracy, rendering quality and computational efficiency. The study aims to bridge the gap between emerging 3DGS research and real-world urban reconstruction requirements, offering insights and guidelines for deploying Gaussian Splatting in practical large-scale scenarios.