3D Gaussian Splatting for Large-Scale 3D Reconstruction: An Evaluation and Quality Analysis

Large-scale 3D reconstruction has emerged as a key research in the fields of photogrammetry and computer vision. 3D Gaussian Splatting (3DGS) has become a mainstream approach due to its efficient rendering, but it confronts critical challenges in large-scale scenarios: excessive memory overhead and inadequate geometric accuracy. Meanwhile, the traditional Structure from Motion and Multi-view Stereo (SfM-MVS) framework, despite its cumbersome process, continues to exhibit robust performance. Notably, a systematic evaluation comparing these two paradigms in large-scale scenes remains absent. To address this, we develop a unified verification framework to evaluate the texture rendering quality and geometric reconstruction precision of several recent methods using real-world datasets. The results indicate that SfM-MVS methods still maintain an advantage in the completeness and accuracy of geometric reconstruction. In contrast, 3DGS methods have achieved breakthroughs in local accuracy or rendering-geometry synergy, yet their global consistency requires further improvement.