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

道路粗糙度分析的3D重建方法比较

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-29 04:00

一篇新研究论文评估了四种3D重建方法——COLMAP、Meshroom、Metashape和3D高斯溅射（3DGS）——在使用智能手机图像分析道路表面粗糙度方面的有效性。研究发现，COLMAP对微纹理最敏感，而Meshroom提供了均衡的重建。Metashape由于内部滤波产生了更平滑的几何形状，而3DGS捕捉到了不规则性，但噪声较高且密度较低。研究结果表明，开源重建流程对于低成本路面监测中的相对粗糙度评估是实用的。 AI

排序理由这是一篇评估不同方法在特定技术任务中表现的研究论文。[lever_c_demoted from research: ic=1 ai=0.4]

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arXiv cs.CV TIER_1 English(EN) · Marouane Elmegdar, Teng Xiao · 2026-05-29 04:00

道路表面粗糙度分析的光摄影测量重建方法与3D Gaussian Splatting的比较评估

arXiv:2605.29452v1 Announce Type: new 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 Splattin…

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道路表面粗糙度分析的光摄影测量重建方法与3D Gaussian Splatting的比较评估

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