# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.9 Segmentation  
Segmentation represented the first group, which needed further separation. We specified three subgroups depending on the processed data type. They were all focused on plain room segmentation but within floor plans, occupancy grids/maps, and point clouds, respectively. For floor plans and occupancy maps, as they are two-dimensional inputs, the results were in the form of room instances segmented on the pixel level of the picture or polygons stretched on sets of points of the analyzed map. This sample scenario is visualized in Figure 13. The point cloud subgroup, as a three-dimensional representation of the whole environment, represented the spatial data. Although more complicated, the segmentation process was quite similar, with the main difference being the dimensionality of the segmented spaces--room instances were described at the point level as clustered sets of points in the point cloud or closed volumetric cuboids. Floor plan analysis was the most represented task. In the found solutions, multiple articles represented the same team of researchers improving presented works, comparing the results, or trying to solve the problem in a different way. The first group of publications were [PERSON] et al. [86; 112] and [PERSON] et al. [87; 91]. Their work initially segmented rooms in a plan with the use of a text segmentation method, followed by wall and object detection. The results were also supported by an Optical Character Recognition (OCR) framework for room label recognition. The later solution introduced patch-based processing, which categorized pixels into one of three classes and constructed a graph of found structures. The analysis of the graph allowed for room segmentation via path planning. The idea was further developed from another perspective in the last article, presenting an Attributed Graph Grammar (AGG) analyzed with a greedy search algorithm for the best possible room segmentation representation. The second solution that improved over the years was the one by [PERSON] et al. [113] and [PERSON] [88; 124]. The initial work presented a probabilistic solution to extracting semantic models from a grid map generated by a Simultaneous Localization And Mapping (SLAM) process. The proposed algorithm, based on the Markov Chain Monte Carlo (MCMC) sampling procedure, searched all possible world models for the best fit (in the biggest likelihood sense). On such a basis, the authors proposed an extended solution that combined the already tested MCMC procedure  
Figure 12: Visualization of the Change Detection task. Initial and updated images (**top**) are analyzed and searched for differences between them. Detected mismatched elements are marked in red (**bottom**). with the idea of a Markov Logic Network (MLN). These were used to prepare a set of rules describing the task-specific context and were reported to improve the performance achieved. Two different approaches to floor plan analysis and environment model generation were presented by [PERSON] et al. [121; 125]. The first article focused on the problem of non-existing standardization in the floor plan format. They stated that research in this field is closely bounded to the processed dataset, and the achieved results are hardly transferable. To solve the issue, they proposed neural network architecture, implementing a style transfer task on the input images based on the idea of Generative Adversarial Networks (GANs). The vectorization process performed on unified floor plan formats was reported to work correctly, even with new, previously unseen forms. The second article dealt with large-scale complex buildings. A patch-based approach was introduced using a CNN architecture to segment the plan and generate a vectorized image that was later used to create a 3D model. The article of [PERSON] et al. [102] used a different approach. It was the first in this task to use the Alfa Shape algorithm to extract rooms from a floor plan.