Practical Evaluation of Standard 3D Gaussian Splatting for Real-World Scene Reconstruction

Abstract

3D Gaussian Splatting (3DGS) enables real-time rendering of photorealistic scene representations from multiview images and has potential as a visualization layer for digital twins. However, photorealistic appearance and image-level metrics alone do not establish practical suitability. We evaluate standard 3DGS on six real-world datasets: wall, forest, piled-pier underside, steel-girder bridge, asphalt pavement, and outdoor sculpture. The framework combines held-out test-view metrics (PSNR, SSIM, and LPIPS), spatial error maps, region-of-interest (ROI) inspection, computational records, scale–opacity grouping, and Gaussian-to-MVS distance analysis. In the wall case study, extending adaptive density control from 7000 to 30,000 iterations nearly doubled Gaussian count and model size and increased training time by about 50%, with only modest test-view improvements. Scale–opacity classes did not reliably distinguish high- from low-error regions. Gaussian-to-MVS distance was more strongly associated with scale than learned opacity, and median local errors generally increased with distance, although distributions overlapped substantially. Across datasets, major structures were generally reproduced well, whereas sparse foliage, low-contrast repetitive textures, fine surface details, and distant background objects required ROI-level verification. The best test-view performance reached 37.11 dB PSNR, 0.952 SSIM, and 0.193 LPIPS. Practical evaluation should consider global quality together with local, computational, and geometric characteristics.