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

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.

Automated summary

A powerful deep learning-based method for reconstructing structure-aware semantic 3D models of cable-stayed bridges is proposed. The approach utilizes multiview images and a photogrammetric point cloud as input, with a recursive binary tree network to model high-level structural relations and low-level 3D geometric shapes. Test results demonstrate successful reconstruction of bridge models with high accuracy.