Structure-aware 3D reconstruction for cable-stayed bridges: A learning-based method

Hu Fangqiao; Zhao Jin; Huang Yong; Li Hui

摘要

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 F-1 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.

机译：本文提出了一种基于强大的基于深度学习的三维（3D）重建缆车座桥的结构感知语义3D模型。通常，当使用低质量点云时，传统的桥式语义3D模型重建方法在不稳定时是不稳定的。此外，它们特别适用于各自的领域，并且对于缆绳座桥的广泛概括。因此，提出了一种结构感知的基于学习的电缆缓存桥3D重构框架。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模拟高级结构关系图和低级3D几何形状。两个实际的斜拉桥是用于评估所提出的方法的例子。测试结果表明，该方法成功地重建了结构组件及其关系的桥梁模型。定量结果表明，预测模型的平均F-1得分为99.01％，倒角距离为0.0259，距离云距为1.78米。所实现的结果类似于在组件方面的准确性方面使用手动重建方法获得的结果，并且它比在空间精度方面使用手动方法获得的结果。此外，所提出的递归二叉树网络对噪声和部分扫描具有鲁棒性。讨论了所获得的3D桥模型的潜在应用。

Structure-aware 3D reconstruction for cable-stayed bridges: A learning-based method

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