Comparative evaluation of photogrammetric reconstruction methods and 3D Gaussian Splatting for road surface roughness analysis

2026-05-28

AI Summary

This study investigates the feasibility of using low-cost smartphone imagery as an alternative to conventional sensors for accurate road surface roughness assessment. Four 3D reconstruction methods—COLMAP, Meshroom, Metashape, and 3D Gaussian Splatting—were systematically compared under a unified post-processing pipeline in CloudCompare, involving point cloud registration, segmentation, normal estimation, and multi-scale roughness computation. Results indicate that COLMAP is most sensitive to micro-texture details, Meshroom offers balanced performance, Metashape produces overly smoothed surfaces due to internal filtering, and 3D Gaussian Splatting captures irregular structures but suffers from high noise and low point density. This work presents the first systematic evaluation of traditional photogrammetry against emerging 3D Gaussian Splatting for pavement micro-texture reconstruction, demonstrating the viability of open-source workflows for relative roughness estimation and offering a cost-effective solution for road monitoring.

Abstract

Image-based 3D reconstruction offers a low-cost alternative to traditional sensor-based techniques for road surface assessment. This study compares four reconstruction pipelines--COLMAP, Meshroom, Metashape, and 3D Gaussian Splatting (3DGS)--to evaluate their ability to estimate road surface roughness from smartphone imagery. All point clouds were processed in CloudCompare using a consistent workflow involving orientation alignment, segmentation, normal estimation, and roughness computation at neighborhood radiuses of 0.2, 0.4, and 0.6 model units. The results show that COLMAP provides the highest sensitivity to micro-texture, while Meshroom yields balanced reconstructions with moderate roughness variation. Metashape produces the smoothest geometry due to its internal filtering, and 3DGS captures visible irregularities but exhibits higher noise and lower density. The comparison demonstrates that open-source pipelines are viable for relative roughness evaluation, offering a practical approach for low-cost pavement monitoring.

Problem

• road surface roughness
• 3D reconstruction
• photogrammetry
• 3D Gaussian Splatting
• smartphone imagery

Innovation

• 3D Gaussian Splatting
• photogrammetric reconstruction
• road surface roughness
• point cloud analysis
• smartphone-based 3D modeling