摄影测量重建方法与3D高斯泼溅用于路面粗糙度分析的比较评估
Comparative evaluation of photogrammetric reconstruction methods and 3D Gaussian Splatting for road surface roughness analysis
AI总结：
本研究比较了COLMAP、Meshroom、Metashape和3D高斯泼溅四种重建方法，评估它们从智能手机图像估计路面粗糙度的能力，结果表明COLMAP对微纹理最敏感，而开源方法适用于低成本路面监测。
摘要：
基于图像的三维重建为传统的基于传感器的路面评估技术提供了一种低成本替代方案。本研究比较了四种重建流程——COLMAP、Meshroom、Metashape和3D高斯泼溅（3DGS），以评估它们从智能手机图像估计路面粗糙度的能力。所有点云均在CloudCompare中使用一致的工作流程进行处理，包括方向对齐、分割、法线估计以及在0.2、0.4和0.6模型单位的邻域半径下进行粗糙度计算。结果表明，COLMAP对微纹理的灵敏度最高，而Meshroom产生具有中等粗糙度变化的平衡重建。Metashape由于其内部滤波而生成最平滑的几何形状，3DGS捕捉到可见的不规则性但表现出更高的噪声和较低的密度。比较表明，开源管道可用于相对粗糙度评估，为低成本路面监测提供了一种实用方法。
英文摘要：
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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