# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 3 Taxonomy of Input Data Types
### Taxonomy Presentation  
The found input data types, as from the first bucketing step of the survey execution protocol, can be seen in Figure 2. Review papers were omitted from the analysis because they do not represent any actual type of input data themselves. The four main types of structures we specified are:  
* 2D Images,
* 3D Spatial Data,
* Graph Structures,
* Mixed Feature Sets. 2D Images were further divided into three and the feature sets into six sub-types of input data structures. In total, twelve types of data were distinguished. In Table 2, we present referenced publications organized by the processed input data type. As 2D images were the most commonly used and are generally the easiest to obtain, we decided to study them more closely. They serve as the primary input data type for most object detectors, are relatively easy to analyze, and are utilized in various scenarios, both academic and non-academic alike. Due to their popularity, flat images are handled in many different ways and by algorithms of various computing complexity. We extend our survey in the direction of 2D images to be able to fully cover their applications and present their popularity in found solutions. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Input Data Type** & **Subtype** & **Found Papers** \\ \hline
3D Spatial Data (68 + 3) & - & [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78] \\ Graph Structure (5 + 2) & Floor Plan / Sketch (26 + 15) & [86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 100; 109; 101; 103; 105; 107; 109; 101; 10  
Figure 2: Diagram presenting first constructed taxonomy—based in article grouping by input data structures. The **2D Images** group represents all the data that could be delivered as images, pictures, document scans, or sketches. It covers the rather raw environment representation, such as its planar depictions, as well as clearly more sophisticated inputs, like real pictures taken by an agent. They visibly differed in the applied processing methodology--with a vision-based approach dedicated to real photos and algorithmic processing for the more symbolic representations. Their essential aspect was that each 2D image could be described as a matrix consisting of columns and rows of numerical data and processed in a pixel-based manner. This way, the group covered a wide range of possible image representations regardless of the number of information channels they provided. It could be a typical black-and-white picture (one channel, 0-255), an occupancy grid map (one channel, empty/occupied/unknown), a colorful image (three channels, RGB, 0-255), or even a depth-aware image (four channels, RGBD, 0-255). As long as the processing remained at the pixel level (was aware of the pixel's x and y coordinates), the input data were classified as a 2D image. To somehow organize the group, we specified three subtypes: floor plan/sketch, occupancy map, and environment picture. The first one covered pictures and scans of the floor plans. They could be images of professional architectural plans from dedicated software or even pictures of freehand sketches. The second subtype grouped occupancy maps generated, e.g., from the laser sensors of home cleaning robots. The third one combined all the pictures of the real indoor environment, photographs taken with a mobile phone, professional indoor panoramas, or frames of videos from surveillance cameras. The **3D Spatial Data** group gathers input data types that somehow describe the spatial positioning of the measurements. This group is dominated by point-cloud-based solutions, so we specified no additional subtypes. Other data structures we found were significantly less numerous, with a 3D mesh model of a building as an example.