# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.1 3D Model Reconstruction  
This group of solutions is focused on generating three-dimensional spatial representations of properties. Depending on the type of processed data, they could take the form of a labeled point cloud, mesh model, or be an upgraded visualization like a floor plan taken from a 2D image into a 3D model by adding the walls with an assumed height as the third dimension (case presented in Figure 5). It is one of the largest groups of articles. **Three-Dimensional Spatial Data** was the most represented input data type. Thirty-four different articles implemented model reconstruction based on it. They all seem very similar, but the difference is the main focus of the processing and implementation details. The largest group of papers represented the task of generating models without additional emphasis on the specification of its concrete type. Such articles produced the least complicated representations, similar to those from CAD-like software wireframe models. Nevertheless, we can further differentiate papers by the exact type of resulting model or the way of acquiring input data. Model generation from point cloud was presented by [PERSON] et al. (2013), [PERSON] et al. (2018), and [PERSON] et al. (2019). All the solutions generate 3D models aware of rooms and their connections described as doors or openings. [PERSON] et al. (2019) paid special attention to the structural constraints of the generated model. They used three levels of constraints, semantic, geometric, and topological, which were introduced to the process of room segmentation for their easier recognition from the point cloud. Similarly, the topological consistency of the generated model was the topic presented by [PERSON] et al. (2018). The first specific model subtype, the mesh model, was developed in the works of [PERSON] et al. (2017) and [PERSON] and [PERSON] (2018). In this representation, a 3D model consists of vertices, edges, and faces that build a mesh of polygons to reconstruct the scanned object. Another subtype of the model was a watertight model. A sample solution generating such was presented by [PERSON] and [PERSON] (2018). A model is classified as watertight when each triangle building the mesh of the model has exactly two neighbors and leads to a structure with no holes. The same resulting model was found in the solutions of [PERSON] et al. (2018) and [PERSON] et al. (2018), with the distinctive aspect being the type of laser scanner--in both articles, a Mobile Laser Scanner (MLS) was used to capture the initial point cloud in a real-time manner. On the market, there are multiple types of scanners available. They all generate the same output, a point cloud, but with different operating concepts. The Standard Static Laser Scanner (SLS) is a ground-based device,  
Figure 5: Visualization of the 3D Model Reconstruction task. From the input 2D image (**left**), there is a simplified 3D model generated (**right**). typically mounted on a tripod placed on a flat surface and in a reference point. It scans the environment with high precision, generating a point cloud including ceilings and floors. It can be moved, but only discretely, from place to place. The MLS changes the approach--it is mounted on a moving vehicle instead. It can be a cleaning robot, a trolley, or a flying drone. This way, the scanning is a relatively continuous operation. SLSs were also used in the papers of [PERSON] et al. (2018) and [PERSON] and [PERSON] (2019). They utilized a specific feature of the scanning process, the trajectory of the scanner. In this way, the point cloud was supported by information regarding consecutive scanner positions in the test environment. A specific subtype of the MLS is a Backpack Laser Scanner (BLS), a version in which a human operator carries the device as a backpack. Such a solution was found in the publication of [PERSON] et al. (2018).