# 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
In all found implementations,the floor clustering was based on the density of points across the height axis of the models. It was effective but introduced a specific limitation--buildings with more complex structures of ceilings were expected to be processed incorrectly, e.g., lofts or rooms with entresol. This problem was partially solved in the work of [PERSON] et al. [54], in which 3D models were used for disaster management and emergency exit path planning. Trajectory, captured by an MLS during scanning, was used in the later processing. The proposed method segmented the trajectory into separated levels and combined the result with scanner position timestamps, allowing the associated parts of the point cloud to be separated as well. All data gathered from mobile devices were processed in three further articles. [PERSON] et al. [76] implemented a solution for collaborative scene modeling using multiple mobile devices compatible with Google Tango technology. One of the proposed applications was to quickly and easily scan a crime scene for further offline investigation of possible clues. A smartphone was also used in the article by [PERSON] et al. [74]. The recorded magnetometer and gyroscope measurements provided values for the azimuth and pitch angles of the device, which allowed researchers to reconstruct the environment with the use of geometry algebra. [PERSON] et al. [75] used an even more sophisticated device. They conducted a survey focused on the possible applications of the Microsoft HoloLens. The device itself is a head-worn mixed reality device consisting of glasses with an integrated IMU, multiple color cameras, depth cameras, and many other sensors. The survey discusses diverse applications of the generated models and acquired measurements: indoor localization, spatial mapping, scene model reconstruction, and semantic segmentation. **Two-Dimensional Images** and solutions generating 3D models from them were the second most numerous. We found five such articles. Four of them involved generating the model from scans of floor plans. As expected, they struggled with specifying the third dimension, namely the height of the rooms. [PERSON] et al. [116] let the user specify this dimension and assumed all components to be equal in height (walls and openings). The work of [PERSON] et al. [94] presented a neural network approach to floor plan parsing, but with the necessary model height provided by the authors themselves. The same was found in the work of [PERSON] and [PERSON] [110], where the height was set to 240 cm. The first paper that actually calculated the height dimension was the one by [PERSON] et al. [111]. They implemented a complex scale calculation procedure based on character recognition in the scanned images. The proposed method first searched for available scale information and (if found) calculated the rest of the model parameters. If the scale was not found, they assumed the median door size to be equal to 90 cm and used this value to calculate the global scale of the floor plan. Basing the model sizing on the proper scale calculations was also found in the work of [PERSON] et al. [154]. They proposed a method to process panorama images of the indoor environment. The lack of scaling information was solved by calculating the scanner distance from the floor and the ceiling. This way, a global scale was found and used for the proper 3D model scaling. The **Feature Set** was the least represented. Based on a feature set of visual cues about the environment, the work of [PERSON] and [PERSON] [195] proposed a complex spatial model, extended with a scanner trajectory and room-type probabilities presentation in a place-by-place manner, that resulted in a 3D metric map of the explored space.