Metric Assessment of 3D Gaussian Splatting for UAV-Based Urban Heritage Reconstruction

Abstract. Urban heritage increasingly faces threats from natural disasters and anthropogenic activities, demanding preservation techniques that balance affordability, speed, and accuracy. Real-time rendering with 3D Gaussian Splatting (3DGS) offers immense potential for rapid heritage documentation; however optimal training configurations especially for aerial nadiral images remain limited. This study compares three 3DGS implementations: native 3DGS, Postshot Splat3, and MCMC variants against conventional multi-view stereo using an 80-meter altitude drone aerial imagery. Results reveal some interesting key findings where moderate downsampling of 1/8 resolution can substantially outperform full resolution training. Postshot Splat3 achieved optimal photometric quality while training 21x faster than native implementations. However, geometric analysis showed that rendering quality and geometric accuracy are not correlated. Furthermore, the result suggested that MCMC configurations failed to compete with native 3DGS and Postshot Splat3. These findings suggest that 3DGS-based heritage documentation requires a rather conservative resolution selection and optimized implementation for efficiency to allow cost effective preservation while maintaining dimensional fidelity.