Learning Structural Graph Layouts and 3D Shapes for Long Span Bridges 3D Reconstruction

Abstract

A learning-based 3D reconstruction method for long-span bridges is proposed in this paper. 3D reconstruction generates a 3D computer model of a real object or scene from images, it involves many stages and open problems. Existing point-based methods focus on generating 3D point clouds and their reconstructed polygonal mesh or fitting-based geometrical models in urban scenes civil structures reconstruction within Manhattan world constrains and have made great achievements. Difficulties arise when an attempt is made to transfer these systems to structures with complex topology and part relations like steel trusses and long-span bridges, this could be attributed to point clouds are often unevenly distributed with noise and suffer from occlusions and incompletion, recovering a satisfactory 3D model from these highly unstructured point clouds in a bottom-up pattern while preserving the geometrical and topological properties makes enormous challenge to existing algorithms. Considering the prior human knowledge that these structures are in conformity to regular spatial layouts in terms of components, a learning-based topology-aware 3D reconstruction method which can obtain highlevel structural graph layouts and low-level 3D shapes from images is proposed in this paper. We demonstrate the feasibility of this method by testing on two real long-span steel truss cable-stayed bridges.

Recovering compressed images for automatic crack segmentation using generative models

“…Such approach is costly and time-consuming to meet the rapidly growing need of structural health monitoring (SHM) around the world. In recent decades, development of vision sensors has allowed continuous collection of image data of civil structures (e.g., automated robots, unmanned aerial vehicles, cars armed with cameras and fixed surveillance cameras in bridges [1][2][3][4][5][6]).…”

3D vision‐based out‐of‐plane displacement quantification for steel plate structures using structure‐from‐motion, deep learning, and point‐cloud processing

“…The algorithm was validated on both Light Detection and Ranging (LiDAR) point cloud and photogrammetric point clouds. Hu et al (2019) proposed a learning-based 3D reconstruction method to determine the structural graph layouts of long-span steel bridges. These studies were focused on the investigation of algorithm applicability in reconstructing civil structures.…”