用手机拍路，四种重建方法测路面粗糙度，效果各有优劣。
Comparative evaluation of photogrammetric reconstruction methods and 3D Gaussian Splatting for road surface roughness analysis
- 对比四款开源重建工具，统一流程评估路面微纹理敏感度。
- COLMAP最敏感，Metashape最平滑，3DGS有细节但噪点多。
- 适合预算有限的公路养护团队做低成本路面监测参考。
基于图像的三维重建为道路表面评估提供了低成本替代方案。本研究比较了四种重建流程——COLMAP、Meshroom、Metashape和3D Gaussian Splatting（3DGS），评估其从智能手机影像中估算路面粗糙度的能力。所有点云均在CloudCompare中采用一致工作流处理，包括姿态对齐、分割、法向量估计，并在邻域半径0.2、0.4和0.6模型单位下计算粗糙度。结果表明，COLMAP对微纹理最敏感，Meshroom生成平衡且粗糙度变化适中的重建结果，Metashape因内部滤波产生最平滑几何，而3DGS虽捕捉到明显不规则性，但噪声更高、密度更低。该比较证明开源重建流程适用于相对粗糙度评估，为低成本路面监测提供可行路径。
原文摘要 · Abstract (English)
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.
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