用无人机与BIM数据自动分割3D结构模型，提升效率与精度
Integrating BIM and UAV-based photogrammetry for Automated 3D Structure Model Segmentation
- 结合真实无人机点云与BIM生成的合成数据进行训练
- 在铁路轨道数据集上准确识别轨枕和钢轨等主要部件
- 小样本加BIM数据可大幅缩短训练时间，适合工程应用
无人机技术的发展使得非接触式结构健康监测更加高效。结合摄影测量法，无人机可获取高分辨率扫描数据，并重建基础设施的详细三维模型。然而，从这些模型中分割出特定结构部件仍是关键挑战，传统方法依赖耗时且易错的人工标注。为此，我们提出一种基于机器学习的三维点云自动分割框架。该方法利用真实无人机扫描点云与建筑信息模型（BIM）生成的合成数据的优势，克服人工标注的局限性。在铁路轨道数据集上的验证表明，该方法能高精度识别并分割钢轨、轨枕等主要构件。此外，通过使用小规模数据集并引入BIM数据，框架显著减少了训练时间，同时保持了合理的分割精度。该自动化方法提升了三维基础设施模型分割的准确性与效率，推动了无人机与BIM技术在结构健康监测与设施管理中的融合应用。
原文摘要 · Abstract (English)
The advancement of UAV technology has enabled efficient, non-contact structural health monitoring. Combined with photogrammetry, UAVs can capture high-resolution scans and reconstruct detailed 3D models of infrastructure. However, a key challenge remains in segmenting specific structural components from these models-a process traditionally reliant on time-consuming and error-prone manual labeling. To address this issue, we propose a machine learning-based framework for automated segmentation of 3D point clouds. Our approach uses the complementary strengths of real-world UAV-scanned point clouds and synthetic data generated from Building Information Modeling (BIM) to overcome the limitations associated with manual labeling. Validation on a railroad track dataset demonstrated high accuracy in identifying and segmenting major components such as rails and crossties. Moreover, by using smaller-scale datasets supplemented with BIM data, the framework significantly reduced training time while maintaining reasonable segmentation accuracy. This automated approach improves the precision and efficiency of 3D infrastructure model segmentation and advances the integration of UAV and BIM technologies in structural health monitoring and infrastructure management.
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