# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 1 Introduction  
Schedule and cost have always been the focus of construction management. Early detection of actual or potential schedule delay or cost overrun provides opportunities for timely adjustments. This requires an automated, timely, and accurate progress-monitoring system to detect deviations between the planned process and the actual performance. In current practice, prevailing monitoring and management systems in the Architecture, Engineering, Construction (AEC) and Facilities Management (FM) industry are still dominated by traditional approaches, including manual paper-based collection and recoding of on-site activities [1; 2]. These procedures are time-consuming, labor-intensive, and error-prone, which cannot be performed as frequently as required. Moreover, current methods may not be conducive to a clear and quick understanding of progress. Because the progress reports in text and graphic format are visually complex, they cannot intuitively reflect information related to space, so it often takes a while for managers to understand the status of progress, which affects the efficiency of information transmission [3]. Building Information Modeling (BIM) is an essential step to digital management of construction projects [4; 5]. BIM creates a three-dimensional (3D) model of building that can be used to represent construction process (4D BIM) by linking activities of a schedule with corresponding building elements. It provides an opportunity to visually compare deviations between the planned process and the actual performance. Recently, several approaches and studies that address the comparison of as-built and BIM-based as-planned data have been presented. The as-built data came from barcoding, Radio-Frequency Identification (RFID), Ultra-Wideband (UWB), Geographic Information System(GIS), Global Positioning System (GPS), laser scanners, image-capturing devices, and so forth. Among them, only laser scanners and image-capturing devices can realize the 3D reconstruction from the construction site to the digital model, which is a necessary part of automated construction progress monitoring in the future. The goal of general 3D reconstruction is to infer the 3D geometry and structure of objects and scenes from one or multiple two-dimensional (2D) images [6]. In the AEC industry, 3D reconstruction of building is the key to connecting the BIM and the project schedule to daily construction images, which enables managers to compare as-planned with as-built status and detect deviations and therefore monitor project progress [7]. At present, 3D reconstruction methods of buildings are mainly divided into two categories: one is to directly generate 3D point clouds of the building through laser scanning, and the other is to take 2D images and then reconstruct a 3D model. 3D laser scanning technology, based on laser ranging principle, can quickly reconstruct the 3D model of the measured object by recording the 3D coordinates, reflectivity and texture of many dense points on the surface of the measured object [8]. This technology has unique advantages in efficiency and accuracy, and is not affected by illumination. However, there are also some shortcomings, such as high cost, high time consumption, and high technical requirements for operation [9; 10; 11]. In this article, the second method, image-based 3D reconstruction, is focused on. The image includes photographs, videos, and depth images. Among them, photographs and videos are RGB images, while depth images are RGB-D images. The spread of camera-equipped devices has promoted the explosion of image data. Massive images and videos were recorded on construction sites daily, and the ever-increasing volume of digital images had inspired scholars to visually capture actual status of construction sites from them. In comparison to other alternatives such as laser scanning, 3D reconstruction from images is at a fraction of cost [12], and the rich color and texture information retained in the image can also be used for semantic recognition and process reasoning.