# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 1 Introduction
Compared with the 3D reconstruction of general objects or the 3D reconstruction of building based on laser scanning, the image-based 3D reconstruction of building has different characteristics and faces special challenges. The research contents of the image-based 3D reconstruction of building can be roughly divided into six processes: image collection, 3D point cloud generation, image-to-BIM alignment, point cloud segmentation, point cloud semantic recognition, and progress reasoning. The main challenges or tasks of each process are as follows:  
1. Although here are many ways to collect images including monocular cameras, binocular cameras, and multi-cameras, the challenges are similar. The intensity of light and shadows seriously affect image quality, and there are many dynamic and static occlusions in addition to self-occlusion at the construction site that prevent researchers from directly observing the building. These factors have brought huge obstacles to 3D reconstruction from images. 2. To generate point cloud from images, the feature points in different images need to be found first. Many algorithms have been studied, such as Scale-Invariant Feature Transform (SITF) [13] and Speeded-Up Robust Features (SURF) [14]. Then, these feature points need to be matched with each other to estimate the fundamental matrixes using algorithms such as Random Sample Consensus (RANSAC) [15]. When the images are taken by a moving camera, it is required to reconstruct the point clouds using Structure from Motion (SfM) [16]. 3. There are various registrations in the process of 3D reconstruction of building, such as 2D-3D and/or 3D-3D registration among images, point clouds, and BIM models. 4. The point cloud generated from images is massy and complicated, which contains background, noise, obstacle, etc. Removing the redundant point cloud and keeping only the Region of Interest (RoI) are beneficial for simplifying data processing and improving calculational efficiency. 5. The point cloud usually only contains 3D coordinate information. To obtain a semantically rich model to infer progress, it is required to identify the type and state of building components represented by each point from the color and texture in RGB images, which is called semantic recognition/labeling of point clouds. 6. Process reasoning is the last step including geometry-based, appearance-based, and relationship-based reasoning. To solve the above challenges and tasks, many scholars have carried out extensive research and produced a variety of intricate methods. In technical articles, however, the review part usually takes the related technologies and methods as the bedding for the follow-up discussion, and does not make a comprehensive analysis of the related technologies. At the same time, most of the review articles tend to focus on one perspective, such as point cloud [17; 18], big data [12; 19], data collection [2; 20; 21; 22], algorithm [23], etc., which does not unify all the methods. Therefore, researchers cannot clearly identify the relationship between various methods to solve the difficulties. This paper will sort out a comprehensive research map, and describe the relevant research results, to provide reference for researchers in the selection of research methods and paths. The goals of this article are three-fold: (1) to integrate the advanced image-based 3D reconstruction methods of buildings to form a research map; (2) to compare the differences among various methods and highlight the advantages and limitations of these methods; (3) to discuss the current challenges of image-based 3D reconstruction of building and explore feasible solutions. What should be noted is that the 3D reconstruction mentioned later is the image-based 3D reconstruction of building; the image refers to photographs, videos, and depth images; the buildings refer to civil infrastructures; and the 3D reconstruction refers to the reconstruction from reality rather than from Computer Aided Design (CAD) drawings. The remainder of this paper is structured as follows.