# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.9 Segmentation
They proposed using a novel convolutional network architecture, LCNet, on indoor maps preprocessed with a watershed-based algorithm. Although deployable to a very specific device and under strict hardware limitations, the reported results were better than the Voronoi- or morphology-based solutions. Point cloud and its segmentation were found in articles published at least a couple of years later than the lower border of the accepted time range. The first one, from 2015, by [PERSON] et al. [16], reported research on creating as-built BIM models. In the article, the authors proposed a method for three-step, three-level segmentation. The first level was the whole floor--the distribution of points along the \(Z\) axis distinguished ceilings and floors. The second level segmented the rooms. First, it took part of the points building the ceiling layer and parsed it into 2D images of separated regions, and second, it applied the room information to all source points. The last processing step was to use the RANSAC algorithm to generally segment planes in the model, which exposed the internal structure of rooms. The reported results were promising, although requiring further research for noise reduction. In 2017, [PERSON] et al. [32] presented a segmentation technique based on the anisotropic potential field. Their solution was reported to be applicable to 3D data representations, like point clouds generated by laser scanners, depth-aware sensor measurements, or even CAD models. The algorithm started with free space detection inside the 3D model. Computed for free voxels, values of anisotropic potential fields were stacked vertically, and maximum values were represented as a 2D map. Clustering of the map performed the actual room segmentation and was finally moved back to the 3D representation by labeling each point with a determined room assignment. [PERSON] et al. [69], in 2017, published the largest found dataset of fully annotated RGBD panoramic images used for scene understanding tasks. The set, namely Matterport3D, containing over ten thousand panoramic views and built from almost 200,000 RGBD images, represents 90 building-scale scenes, their panoramic skyboxes, textured meshes, scanner locations, and other features, including point cloud representations. During the dataset presentation, not only data but also its application was presented--solutions to sample tasks of keypoint matching, view overlap prediction, estimation of the surface normal, region-type classification, and semantic voxel labeling. Regarding room segmentation, it was a CNN-based approach for scene classification. Sample baseline results were achieved with the use of ResNet-50 [207]. In 2018, [PERSON] et al. [34] proposed combining (in the process of space subdivision) data from the point cloud and the scanner's trajectory. They used an Indoor Mobile Laser Scanner (IMLS) to capture the initial point cloud and record the scanning trajectory. First, the trajectory was used to detect staircases and separate stories--in the algorithm, timestamp-ordered trajectory points are assumed to represent the same segment based on the detected height. A later combination of trajectory and points analysis allowed for doorway detection, which finally separated the rooms. In the article by [PERSON] et al. [38], the methodology was also tested with additional attention placed on reflective surfaces causing noise in the data and glass walls influencing the measurements. Also, in 2018, the work of [PERSON] et al. [39] researched a similar setup in the space subdivision task based on the scanline analysis process. The paper presented a novel method for opening extraction that searched for geometric regularities in scanlines and returned opening candidates. Based on the opening detections, the trajectory became segmented, and results were populated to the whole point cloud. In 2019, [PERSON] et al. [41] presented a fully automatic solution, performing unstructured point cloud segmentation through graph cuts based on semantic constraints.