Comparative evaluation of photogrammetric reconstruction methods and 3D Gaussian Splatting for road surface roughness analysis
For researchers and practitioners in low-cost pavement monitoring, this work provides a comparative evaluation of open-source and commercial reconstruction methods for road roughness analysis.
This study compares four photogrammetric reconstruction pipelines (COLMAP, Meshroom, Metashape, 3DGS) for estimating road surface roughness from smartphone imagery, finding that COLMAP offers highest micro-texture sensitivity, Meshroom balanced reconstructions, Metashape smoothest geometry, and 3DGS higher noise with lower density.
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.