# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 3 Technology Path of Image-Based 3D Reconstruction
### Image Collection and 3D Point Cloud Generation
#### 3.2.2 Data Type  
In different studies, the form of images is related to the acquisition equipment and affects the selection of subsequent methods. There are three main forms used by most researchers. 1. Time-lapse images/videos from fixed camera: Fixing the camera position means simplifying complicated registration process. As long as the camera coordinates and camera shooting direction are obtained, the images can be registered with the BIM model after simple rotation and scaling. Although this means a lack of flexibility in response to occlusions caused by changing structures, the benefits of always-on-demand images provide the possibility for fast and responsive assessment [32]. However, to reduce occlusion, it is necessary to increase the number of cameras shooting from multiple angles [33; 34], as shown in Figure 2, which raises new questions--how to arrange multiple cameras and how to deal with data conflicts between cameras. In addition, the cameras need to be fixed on a stable object, which sometimes proves difficult. In addition, [PERSON] et al. [33] found that small errors will significantly affect registration and minimize the allocated image area for each element, making the task of recognition much more challenging. 2. Unordered image sets: Unordered image sets can be taken from any location, so that almost all corners can be captured without occlusions. These images are usually taken by construction managers, owner representatives, contractors, and subcontractors and have capacity to enable complete visualization of a construction site [3]. However, developing computer vision and image processing techniques that can effectively operate on such imagery is a huge challenge [3]. [PERSON] et al. [3; 35] came up with a way--extract SIFT feature points from continuous images, match them to estimate the fundamental matrixes using the RANSAC algorithm, and use the SfM principle to generate point clouds, as shown in Figure 3. In this method, although the image sets can be unordered, the images in an image set are orderly, and a certain proportion of repeating regions among these images is needed to extract corresponding SIFT points. In addition, the user needs to initially register the as-planned and as-built models [35]. When there are many unordered image sets, it is necessary to manually record the camera position and external parameters, and each image set requires an initial registration that is quite troublesome. Moreover, to avoid occlusion and cover all observed objects, a large amount of overlap is necessary, which is almost impossible for manual acquisition. Some researchers use camera-equipped Unmanned Aerial Vehicles (UAVs) to professionally take images and document them [36; 37], which allow for a wider range of views, especially from above, and the GPS coordinates and camera orientation are known in most cases. Even so, it is still very difficult and is tedious to find the exact views in BIM due to the inaccurate GPS coordinates especially in the vertical axis [12]. 3. Depth images: Depth images, generated from range cameras/RGB-D cameras, contain not only RGB colors but also depth information, as shown in Figure 4. Similar \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Device** & **Raw Data Type** & **Cost** & **Technical** & **Portability** & **Resolution** & **Texture** & **3D Surfaces** \\  & & & & & & **Representation** & **Reconstruction** \\ \hline Camera & Photograph & Low & Low & High & High & Rich & - \\ Smart & Photograph & Low & Low & High & High & Rich & - \\ devices & Photograph & Low & Low & High & Rich & - \\ Monitor & Video & Low & Low & Low & Medium & Rich & - \\ UAV & Video & High & High & Low & Medium & Rich & - \\ Laser scanner & Point cloud & High & High & Low & High & Limited & Automatic \\ Depth & Photograph and & Medium & Low & High & Rich & Automatic \\ camera & Remote sensing & High & Low & - & Low & Medium & - \\ Satellite & & images & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Device performance comparison.