To advance the scan-to-model process for steel girder bridges, this paper presents an automated approach for creating complete geometric models for the steel superstructure elements based on the segmentation results from Yan and Hajjar (2021). The key innovation is two-fold: 1) cross-frames are automatically partitioned into cross-frame members that need to be modeled separately; 2) a 3D occlusion labeling algorithm is developed to identify occluded spaces, which are subsequently used in the modeling process to mitigate the effects of occlusions. The proposed approach is validated using real-world point clouds collected from a highway bridge, and the validation indicates that the errors in computing element widths and thicknesses are less than ±5% and ± 8% respectively, provided a reasonable data completeness. As the level of data completeness decreases, the errors for element widths can still be kept less than ±5%, but the element thicknesses can be overestimated by up to 52%.

为了推进钢梁桥的扫描到模型过程，本文提出了一种基于 Yan 和 Hajjar (2021) 的分割结果为钢上部结构元素创建完整几何模型的自动化方法。关键创新有两点：1）横梁自动划分为需要单独建模的横梁构件；2) 开发了一种 3D 遮挡标记算法来识别遮挡空间，随后在建模过程中使用这些算法来减轻遮挡的影响。使用从高速公路桥梁收集的真实世界点云验证了所提出的方法，验证表明计算元素宽度和厚度的误差分别小于±5％和±8％，提供了合理的数据完整性。