用合成点云训练模型，自动分割桥梁构件，提升检测效率。
Instance Segmentation of Reinforced Concrete Bridges with Synthetic Point Clouds
- 通过三种方法生成合成点云数据，解决真实标注数据少的问题。
- 在真实激光雷达与摄影测量点云上达到顶尖性能，准确分割桥梁构件。
- 适合智能交通、基础设施巡检领域研究者与工程人员参考。
国家桥梁检查标准要求对桥梁进行详细的元件级检查。传统上，检查员需人工根据损伤情况评定结构部件的状况等级，这一过程耗时且劳动强度大。自动化元件级桥梁检查可促进更全面的状态记录，从而提升整体桥梁管理效率。尽管已有研究探索了桥梁点云的语义分割，但针对桥梁构件实例分割的研究仍有限，部分原因在于缺乏标注数据集，以及训练模型难以泛化。为此，本文提出一种新颖的合成数据生成方法，采用三种不同技术。该框架基于Mask3D Transformer模型，通过超参数调优和一种新型遮挡技术进行优化。在真实激光雷达（LiDAR）与摄影测量点云上均取得当前最优性能，验证了该框架在自动化元件级桥梁检查中的潜力。
原文摘要 · Abstract (English)
The National Bridge Inspection Standards require detailed element-level bridge inspections. Traditionally, inspectors manually assign condition ratings by rating structural components based on damage, but this process is labor-intensive and time-consuming. Automating the element-level bridge inspection process can facilitate more comprehensive condition documentation to improve overall bridge management. While semantic segmentation of bridge point clouds has been studied, research on instance segmentation of bridge elements is limited, partly due to the lack of annotated datasets, and the difficulty in generalizing trained models. To address this, we propose a novel approach for generating synthetic data using three distinct methods. Our framework leverages the Mask3D transformer model, optimized with hyperparameter tuning and a novel occlusion technique. The model achieves state-of-the-art performance on real LiDAR and photogrammetry bridge point clouds, respectively, demonstrating the potential of the framework for automating element-level bridge inspections.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。