# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 3 Technology Path of Image-Based 3D Reconstruction
### Image-to-BIM Alignment  
The existing registration (or alignment) methods for 3D reconstruction can be categorized into two forms: one is the registration between homogeneous partial data to form a global model, including image-image (2D-2D) and point cloud-point cloud (3D-3D) alignment; the other is the registration between different types of data, such as image-BIM (2D-3D), point cloud-BIM (3D-3D), and/or image-point cloud-BIM (2D-3D-3D) alignment. Since these alignment processes start from the original data (images) and finally to BIM, the whole process will be called image-to-BIM alignment in this article. Figure 4: A photo taken from the construction site and its depth map [40]. Figure 3: Left to Right: Visualizing keypoint matching between a pair of images shown as **(a)** Image1-Before RANSAC, **(b)** Image1-After RANSAC, **(c)** Image2-Before RANSAC, **(d)** Image2-After RANSAC [3]. Figure 2: Schematic process for image-based 3D point cloud generation [17]. To analyze the construction performance, an as-is condition needs to be compared to an as-planned condition [12]. The image-to-BIM alignment intends to make the acquired images comparable to the as-planned information contained in BIM [4]. There are four approaches proposed in recent years to support image-to-BIM alignment. 1. 3D-2D registration-based: Monitoring the construction process using fixed cameras without pan/tilt/zoom is one of the most convenient ways. Because once the user initially registers the as-planned and as-built models, the correspondence between the photograph and the virtual model would be set for all subsequent images [33]. Many scholars superimpose 3D visual models on images in Augmented Reality (AR) or Visual Reality (VR) environments [3; 33; 41]. Ideally, all visual models could be projected on the image plane and fully registered with the image. However, the outdoor camera is susceptible to environmental influences such as gravity and transverse winds, which can easily lead to the failure of automatic registration. Therefore, a set of key points with known positions in the photograph and the 3D visual environment is required to achieve more accurate registration [32]. 2. Feature point-based: To avoid the problem of occlusion caused by fixing cameras, some scholars explore the methods of movable cameras. [PERSON] et al. [3; 35] studied a method of extracting SIFT feature points from unordered image sets. By identifying the common feature points of overlapping region, these images were registered with each other to generate feature point cloud. Then, the images and the virtual model were registered by aligning the feature point cloud with the 3D virtual model in 4D Augment Reality (D4 AR) environment. They realized the image-to-BIM alignment through the registration of image-to-point cloud and point cloud-to-BIM. In addition, many automated methods have also been proposed to register point clouds with BIM models. [PERSON] et al. [42] presented a novel method (4-plane congruent set algorithm) for automatic registration of as-is 3D point clouds with 3D BIM models. [PERSON] et al. [43] proposed a 3D patch registration approach based on Convolutional Neural Network (CNN) deep-learning algorithm for integrating sequential models in support of progress monitoring. 3. Depth image-based: Compared with the above method, the image-to-BIM alignment method based on the depth image simplifies the process of image registration and point cloud generation because the depth information is included in the depth image. In the research of [PERSON] et al. [38], workers captured all workplaces inside and outside of the building in real time and record partial point clouds, their locations, and time stamps by Kinect (helmet-mounted scanner). Then by manually picking the equivalent points, the partial point clouds were registered and merged into a complete 4D as-built point cloud of a building under construction. Finally, the image-to-BIM alignment was realized using a software developed at the University of Maribor [10].