In this paper, we present a case study that performs an unmanned aerial vehicle (UAV) based fine-scale 3D change detection and monitoring of progressive collapse performance of a building during a demolition event. Multi-temporal oblique photogrammetry images are collected with 3D point clouds generated at different stages of the demolition. The geometric accuracy of the generated point clouds has been evaluated against both airborne and terrestrial LiDAR point clouds, achieving an average distance of 12 cm and 16 cm for roof and facade respectively. We propose a hierarchical volumetric change detection framework that unifies multi-temporal UAV images for pose estimation (free of ground control points), reconstruction, and a coarse-to-fine 3D density change analysis. This work has provided a solution capable of addressing change detection on full 3D time-series datasets where dramatic scene content changes are presented progressively. Our change detection results on the building demolition event have been evaluated against the manually marked ground-truth changes and have achieved an F-1 score varying from 0.78 to 0.92, with consistently high precision (0.92 - 0.99). Volumetric changes through the demolition progress are derived from change detection and have shown to favorably reflect the qualitative and quantitative building demolition progression.

翻译：在本文中,我们提出了一个案例研究,分别对基于3D型微缩变化探测和监测拆除事件期间建筑物逐渐崩溃性能的3D型无人驾驶飞行器(UAV)进行精确的3D变化探测和监测;用拆房不同阶段产生的3D点云收集多时倾斜摄影测量图象;对空载和陆载LIDAR点云进行了对生成点云的几何精确度的评估,分别达到屋顶和表面平均距离12厘米和16厘米;我们建议了一个分级体积变化探测框架,将多时空UAV图像统一起来,以作出估计(无地面控制点)、重建,并进行粗略至fine 3D密度变化分析;这项工作提供了一种解决办法,能够在3D全时间序列数据集显示剧情变化的情况下进行改变探测;对建筑拆房事件的变化探测结果进行了人工标明的地面图理变化,并取得了从0.78到0.92至0.92的F-1分,且始终具有高精确度(0.92-0.99)的建筑量变化,通过拆房进展的量位变化从检测到定性反应,并显示质量变化。