A topology-aware 3D reconstruction algorithm for long-span cable-stayed bridges

3-Dimensional reconstruction (3D reconstruction) generates a 3D computer model of a real object or scene from data such as images, it involves many stages and open problems. Existing methods focus on point clouds and reconstructed polygonal mesh within Manhattan-world constrains in urban scenes reconstruction. However, when dealing with structures like steel truss cable-stayed bridges with complex topology (i.e., connectivity and genus), existing methods fail to recover an appealing polygonal mesh from highly unstructured and noisy point clouds. A topology-aware 3D reconstruction method which can obtain high-level structures and low-level shapes is proposed in this paper. A convolutional neural network and point cloud network is designed to encode multi-view images and 3D point cloud into a compact code, which is then decoded into structure layouts (i.e., a hierarchical binary structural parsing tree) and 3D shapes (i.e., leaf nodes on the binary tree) by designing a recursive neural network and a distance field network respectively. These high-level structures and low-level shapes constitute a 3D digital model.