# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Summary and Discussion  
In this section, we present a brief discussion of the discovered dependencies and observed tendencies. The number of implementations found is too large to summarize them all at once, so we do it task-by-task. A general discussion is held at the very end. **3D Model Generation**. We can distinguish highly professional solutions (dedicated to commercial use) and solutions based on simplifications (often intentional, aimed at reaching non-professional users). The creation of a three-dimensional model was best carried out using three-dimensional data. In our opinion, the best results were achieved through point cloud processing. Such input is the best reflection of the actual world (the most precise), and its influence on the quality of the results was visible. Working with two-dimensional data lacks information about the third dimension, and its recovery is always fraught with errors. In one-dimensional data, it was impossible to recreate the model at all. Subjectively, the best solution currently achievable is a combination of algorithmic point  
Figure 15: Visualization of the Map Alignment task. A set of misaligned input plans (**left**) is combined into a single aligned result (**right**). cloud preprocessing and machine-learning-based postprocessing. The first approach would achieve some kind of data preparation and cleanup, e.g., clustering and outlier removal. The latter would retrieve the actual environment structure. **Content-Based Image Retrieval (CBIR)** was a relatively rare task, even though searching by image seems to be gaining popularity in the context of type recognition and floor plan analysis. All the solutions we found were processing the data in the same way. First, they analyzed the input image, then created a graph representation of it, and finally compared the structure for similarities. The obtained results were satisfactory, but we see a research niche here for some more sophisticated processing, especially for further development in the direction of recommendation systems. **Environment Description Creation** is still not popular yet is a very important topic, especially for visually impaired people. The solutions we found in this area were implemented in disability support systems and were based solely on processing room plans, mainly using neural networks. There are unexpectedly few works in this research field and with a narrow scope of processed inputs. We can see great potential for developing systems that generate descriptions based on three-dimensional data. They could significantly improve the precision of generated explanations and reduce data loss, e.g., by providing details about the height of tables, countertops, chairs, or the steps of stairs. **Floor Plan Vectorization** and its emergence was purely caused by the imperfections of the input data. By definition, raster images of floor plans do not scale well and lose quality while enlarging. The easiest solution would be to change the acquisition method. However, this is not always possible because vectorization is commonly used in digitizing existing documents. The works found here were trying to retrieve the lost information and switch the image format to a scalable one. This task was dominated by machine learning, mainly covering neural networks. Subjectively, the best results are currently achieved by Generative Adversarial Networks (GANs). **Floor Plan Prediction/Generation**. We observed an immense expansiveness of the quality of the results. This was caused by the processing of data with virtually any degree of complexity. The most straightforward and least accurate predictions were achievable even on one-dimensional data, although the most accurate mapping was performed using three-dimensional input. With the use of spatial data, it was also the easiest one to implement. Technically, obtaining a 2D plan from a point cloud or 3D model is a kind of simplification. In such a case, we suggest moving from a more information-rich representation to a simpler one.