# 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
They successfully used indoor point cloud data gathered by a BLS to generate a geometric representation of found rooms, utilizing a two-step procedure based on the data refinement and later processing. [PERSON] et al. (2018) and [PERSON] et al. (2019) targeted the creation of 'as-built' BIM from 3D point clouds. This means that they processed data for buildings that did not have a proper 'as-designed' BIM created during the construction. A similar application to the generated model was described by [PERSON] et al. (2018), but as just one of many possible applications of volumetric models. The BIM generation was also further investigated by [PERSON] et al. (2019). Their solution focused on the generated output files. They processed LiDAR data, prepared a BIM model, and saved it to a gbXML file representing an energy-efficiency-focused structure, which could be used in further energy studies. [PERSON] et al. (2018); [PERSON] et al. (2018) and [PERSON] et al. (2019) discussed model generation in highly cluttered input data scenarios with severe occlusion. They presented scenarios of, e.g., wall parts placed behind obstacles, large-scale scan artifacts, or outlier points in the acquired point cloud. Many publications implicitly or explicitly work with the Manhattan-world assumption ([PERSON] et al., 2018) (highly regular environment). It can be used in building structure simplification. One example is the article of [PERSON] et al. (2018). They discussed indoor layout regularization and proposed a method for room hierarchy estimation, assuming their high similarity and regular shapes. On the other hand, the work of [PERSON] et al. (2019) explicitly focuses on processing multi-room building structures with curved walls, which is not a typical layout. They proposed a solution using horizontal slicing of the acquired point cloud, which allowed them to easily ignore the furniture present in the building and simplified the detection of straight and curved walls. Object detection and classification can also be separate topics. [PERSON] et al. (2019) proposed segmenting the point cloud into \"disjoint spaces\", representing objects found in the building, such as chairs, sofas, or tables. They detected gaps in the density of recorded points and deduced the potential edges of separated objects. [PERSON] et al. (2019) and [PERSON] et al. (2019) presented solutions to a highly different task. Both proposed methods for autonomous robot exploration of an unknown environment. The main problem was to somehow plan an efficient path and set of scanning points so that a robot could acquire the most precise model of the building. Similarly, a mobile robot was used by [PERSON] et al. (2019) to create a thermal model of the explored building. The achieved solution augmented the captured point cloud with colors that indicated the temperature of each of the points. By grouping them, it was possible to reveal potential heat sources. In their implementation, they used the idea of space voxelization. A voxel can be described as being like a pixel of an image but in a three-dimensional world. It is the smallest part of a regular, grid-adjusted volume. The topic of voxelization was also found in the work of [PERSON] et al. (2019). For an indoor environment, their suggested solution for model reconstruction is voxelization with a 5 cm resolution. This way, the generated grid was dense enough to successfully reconstruct even complex room layouts, like those with curved walls, complicated openings, or a height of more than one floor. The generation of multistory building models was found as the topic of papers by [PERSON] et al. (2019), [PERSON] et al. (2019), and [PERSON] et al. (2019). They all process massive amounts of data, as each consecutive level of scanned buildings multiplied the number of acquired points. Implemented approaches are quite similar. A typical process segments the whole building into separated floors first and into distinctive rooms second.