Image-based 3D reconstruction for Multi-Scale civil and infrastructure Projects: A review from 2012 to 2022 with new perspective from deep learning methods

Abstract

As a bridge between physical objects and as-built models, image-based 3D reconstruction performs a vital role by generating point cloud models, mesh models, textured models, and eventually BIMs from images. This study provides a quantitative and qualitative summary of image-based 3D reconstruction for civil engineering projects in the last decade. A bibliometric analysis of 286 journal papers suggested that 3D reconstruction is an interdisciplinary field that integrates photogrammetry, 3D point cloud analysis, semantic segmentation, and deep learning. Based on the analysis, we proposed a 3D reconstruction knowledge framework with three dimensions — essential elements, use phases, and reconstruction scales. The “essential elements” dimension is a technical framework of visual geometry and deep learning methods for 3D model generation. The “use phases” emphasize using 3D reconstruction techniques during the construction, operation, and maintenance phases, which are driven by the demands of visual inspection in various contexts. The “reconstruction scales” dimension synthesizes 3D reconstruction applications from the component level to the city scale with highlights of their opportunities and challenges. This 3D reconstruction knowledge framework sheds light on eight future research directions: automated modeling, model fusion, performance optimization, data fusion, enhanced virtual experience, real-time modeling, standardized reference, and in-depth deep learning research. This review can help scholars understand the present status and highlight research trends of image-based 3D reconstruction in civil engineering associated with the integration of deep learning methods.