# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 3 Technology Path of Image-Based 3D Reconstruction
### Progress Reasoning  
Progress reasoning is a key approach that compares the as-panned model with the as-built model and detects deviation between them. In the last few years, many methods for progress reasoning have been proposed. These reasoning processes are based on geometry, appearance, relationship, and so on. In this paper, these methods are classified into four categories:  
1. Based on the 3D space occupancy by the point clouds: [PERSON] et al. [25] split the BIM element surface into 2D raster cells and verified the progress information by the number of points extracted for each raster cell within a certain distance before and behind the BIM element surface. [PERSON] et al. [1] created internal and external surface planes for BIM model and measured the true column heights by the point cloud between the external and internal surface boundaries. [PERSON] et al. [35] traversed and labeled for expected progress visibility and a machine-learning scheme built upon a Bayesian probabilistic model was proposed that automatically detects physical progress. 2. Based on the 2D plane projection of the point clouds: [PERSON] et al. [10] and [PERSON] et al. [38] projected a BIM element to three orthogonal planes and rasterized them within a regular grid. Then, they projected the points in the element's proximity onto the same grids and the area of grid-cells containing projected points is considered to be a covered area. Finally, they identified the existing elements by assessing the percentage of elements' surface being covered by the point cloud. [PERSON] et al. [29] projected the point clouds onto a plane which runs parallel to the floor generating a heat map, from which a closed loop providing the room's floor plan were construct, as shown in Figure 8. On this basis, 3D points were projected onto the walls creating an image per wall to extract contours which were characterized into windows, doors, or other apertures. 3. Based on the image changes of 3D-2D projection area: [PERSON] et al. [60] applied 3D CAD-based image mask filters to identify the construction progress of a cable-stayed bridge on background with little noise, which may not be appropriate for complex environments. [PERSON] and [PERSON] [61] identified the segmented image region using machine-learning techniques to determine whether the region was composed of concrete or not. The concrete identified by this method is a whole area, not refined to the component. For this defect, [PERSON] et al. [32] segmented the image into a set of discrete component masks and analyzed the texture or color changes of specific regions of interest related to each component to infer the timings of significant events. Unfortunately, most of these changes were related to spurious lighting and other variable conditions, such as equipment, or scaffolding being moved. Then [PERSON] and [PERSON] [30] proposed a new appearance-based material classification method for monitoring construction progress deviations at the operational-level. They used pre-trained multiclass material classifier to recognize the texture of the region of inter  
Figure 7: The construction material library [59]. est, rather than only based on the change of color. Afterward, [PERSON] et al. [62] combined the geometry-based and appearance-based reasoning methods for detecting construction progress, which had the potential to provide more frequent progress measures. 4. Based on the relationships of geometric primitives: Sometimes occlusions are inevitable. It is a wise choice to use auxiliary information to reason progress, because it can greatly reduce the duplication of effort in the data collection phase and the ambiguity of recognition results. The auxiliary information includes physical relationships (aggregation, topological and directional relationships) and logical relationships between objects or geometric primitives. [PERSON] and [PERSON] [63] represented the knowledge model of the spatial relationships with a semantic net. [PERSON] et al.