Extracting Structural Models through Computer Vision

The ability to accurately and rapidly assess structural integrity after a disaster is critical from both a safety and economic perspective. Existing post-disaster inspection methods are time-consuming and expensive, requiring highly trained inspectors to travel to target sites and manually collect data. Automated analysis of civil structures from visual data through computer vision can be used to improve the level of accuracy in the condition assessment procedure. This paper presents a method of automated and systematic computer vision-based structural analysis. It uses a set of digital photographs to produce a 3D model through Structure from Motion (SfM) algorithms, followed by fully automated recognition and assembly of structural elements (e.g., columns and beams) from the image-based 3D dense reconstruction of the structure. There are three key challenges in this work: (i) proper 3D mesh segmentation, (ii) robust computer vision algorithms for isolating different structural components, and (iii) classification and localization of damage that is present in the 3D model. As the part of the proposed system, extracted information from the dense 3D model is used to assemble the structural elements and create a Finite-Element Method (FEM) model. Lastly, a supervised machine learning scheme built upon a large and comprehensive data set is used to automatically update the model to account for damage. The proposed methodology has applications beyond post-disaster condition assessment, from routine inspection to infrastructure management applications.