# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 4 Knowledge Gaps and Challenges
### Occlusions and Limited Visibility  
In the implementation of 3D construction, occlusions are inevitable and the most challenging issues that must be addressed. Occlusion is defined as any blockage of the camera vision by a physical object [65], which results in incomplete data and challenge reasoning under limited visibility [30]. Occlusion can be classified into two main categories based on its source, static occlusions which are self-occlusions caused by progress itself (e.g., a facade blocking the observation of elements in the interior) or occlusions caused by temporary structures (e.g., scaffolding or temporary tenting), and dynamic occlusions which is a result of movable objects (e.g., laborers, machines, etc.) [35], as shown in Figure 9.  
Figure 8: Results of wall detection. Visualization of a heat map provided by projecting the point cloud onto the floor (lightened up to improve visibility) (left). The analyzed heat map (right) [29].  
To reduce dynamic occlusion, [PERSON] et al. [1] decided to capture site photos after the duty time (i.e., after 5:00 p.m.). The selected time significantly reduced the dynamic occlusions for captured photos because the site was shut down and there are no active laborers or machines.  
Compared with the dynamic occlusions, static occlusions are unavoidable. In particular, time-lapsed images or videos from fixed camera only show what is within range and field-of-view of the camera [3]. [PERSON] et al. [33] described two different scenarios on horizontal and vertical occlusions and the challenges of visualizing progress only on a single view. To reduce the occlusion, a network of multiple cameras was used to realize the coverage of the whole building [34]. [PERSON] et al. [33] suggested finding the optimum location of a network of cameras to make sure all the elements could be monitored [33]. However, it still cannot be used to track progress inside the building after the building envelope is placed.  
This motivated scholar to use an unordered set of progress imagery that is taken from various viewpoints to tackle the occlusion issue [3,33]. These images and videos collected by digital cameras and smartphones were usually taken by field personnel, including construction managers, owner representatives, contractors, and subcontractors. Although they have the capacity to enable complete visualization of a construction site, these images are typically uncalibrated and their locations and orientations are unknown, which makes it very hard to accurately localize them with BIM [12].  
In addition to the above direct avoidance of occlusion, scholars have also proposed some indirect methods to avoid the effects of occlusion. One approach is to use prior knowledge, for example, the projections of BIM models to define the RoI to guide the process of identifying [12,32]. In this case, a lot of occlusions can be ignored because the result can be obtained as long as a certain proportion of the target area meets the recognition requirements. In addition, in recent studies, advanced deep-learning technology has been used to identify construction components in images, which can deal with partial occlusion [66]. These methods can only be used for partial occlusion rather than full or almost all occlusions. In the more negative case, the semantic net describing the spatial and logical relationships between objects of geometric primitives can be used [63]. Objects that are easily recognizable can be detected first, and then more challenging structures can  
Figure 9: Occlusions [35].  
be inferred using the information in semantic net [24]. In addition, the semantic net can also provide an effective way to verify the recognition results.