A powerful deep learning‐based three‐dimensional (3D) reconstruction method for reconstructing structure‐aware semantic 3D models of cable‐stayed bridges is proposed herein. Typically, conventional bridge semantic 3D model reconstruction methods are not robust when low‐quality point clouds are used. Furthermore, they are suited particularly for their respective fields and less generalized for cable‐stayed bridges. Hence, a structure‐aware learning‐based cable‐stayed bridge 3D reconstruction framework is proposed. The encoder part of the network uses both multiview images and a photogrammetric point cloud as input, whereas the decoder part uses a recursive binary tree network to model a high‐level structural relation graph and low‐level 3D geometric shapes. Two actual cable‐stayed bridges are employed as examples to evaluate the proposed method. Test results demonstrate that the proposed method successfully reconstructs the bridge model with structural components and their relations. Quantitative results indicate that the predicted models achieved an average F₁ score of 99.01%, a Chamfer distance of 0.0259, and a mesh‐to‐cloud distance of 1.78 m. The achieved result is similar to that obtained using the manual reconstruction approach in terms of component‐wise accuracy, and it is considerably better than that obtained using the manual approach in terms of spatial accuracy. In addition, the proposed recursive binary tree network is robust to noise and partial scans. The potential applications of the obtained 3D bridge models are discussed.

中文翻译：

---

斜拉桥的结构感知3D重建：一种基于学习的方法

本文提出了一种强大的基于深度学习的三维（3D）重建方法，用于重建斜拉桥的结构感知语义3D模型。通常，当使用低质量的点云时，常规的桥语义3D模型重构方法并不可靠。此外，它们特别适合各自的领域，而不太适用于斜拉桥。因此，提出了一种基于结构的学习型斜拉桥3D重建框架。网络的编码器部分使用多视图图像和摄影测量点云作为输入，而解码器部分使用递归二叉树网络来建模高级结构关系图和低级3D几何形状。以两个实际的斜拉桥为例来评估所提出的方法。测试结果表明，该方法成功地重建了结构构件及其相互关系的桥梁模型。定量结果表明预测模型达到了平均水平F ₁得分为99.01％，倒角距离为0.0259，网格到云的距离为1.78 m。就组件精度而言，所获得的结果与使用手动重建方法所获得的结果相似，就空间准确性而言，所获得的结果要比使用人工方法所获得的结果要好得多。另外，提出的递归二叉树网络对噪声和部分扫描具有鲁棒性。讨论了获得的3D桥梁模型的潜在应用。