优化无人机航拍参数，用更少照片更快完成屋顶三维建模。
Evaluation of Flight Parameters in UAV-based 3D Reconstruction for Rooftop Infrastructure Assessment
- 通过控制地面采样距离和图像重叠率，优化飞行参数。
- 0.75-1.26厘米GSD配合85%重叠率达高精度建模。
- 适合需要高效无人机巡检的市政与建筑评估场景。
基于无人机摄影测量的屋顶三维重建为基础设施评估提供了有效方案，但现有方法常需高图像重叠度和较长飞行时间以保证模型精度。本研究系统评估了地面采样距离（GSD）与图像重叠率等关键飞行参数，以优化复杂屋顶结构的三维重建。在女王大学多段屋顶上使用DJI Phantom 4 Pro V2进行受控飞行，设置不同GSD与重叠率组合。数据经RealityCapture处理，并与基于无人机激光雷达和地面激光扫描生成的真值模型对比。结果表明，在0.75–1.26厘米GSD范围搭配85%图像重叠时，可实现高精度重建，同时显著减少拍摄图片数量与飞行时间。研究为自主无人机航线规划提供了实用指导。
原文摘要 · Abstract (English)
Rooftop 3D reconstruction using UAV-based photogrammetry offers a promising solution for infrastructure assessment, but existing methods often require high percentages of image overlap and extended flight times to ensure model accuracy when using autonomous flight paths. This study systematically evaluates key flight parameters-ground sampling distance (GSD) and image overlap-to optimize the 3D reconstruction of complex rooftop infrastructure. Controlled UAV flights were conducted over a multi-segment rooftop at Queen's University using a DJI Phantom 4 Pro V2, with varied GSD and overlap settings. The collected data were processed using Reality Capture software and evaluated against ground truth models generated from UAV-based LiDAR and terrestrial laser scanning (TLS). Experimental results indicate that a GSD range of 0.75-1.26 cm combined with 85% image overlap achieves a high degree of model accuracy, while minimizing images collected and flight time. These findings provide guidance for planning autonomous UAV flight paths for efficient rooftop assessments.
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