# A Review on Mixed Reality: Current Trends, Challenges and Prospects
## 3 Concept of Mixed Reality
### Algorithm
#### 3.2.2 Model of Space and Simulation  
Implementing any MR scenario requires real space modeling to create the MR environment. The space modeling contains two parts: modeling a large space such as a room, and real object such as tissue. The modeling of a large space such as a room should preserve constraints such as relationships related to room's objects and users. The simulation of a real-world object such as tissue must consider constraints such as the modeling deformation of the object. Ref. [32] used geometry, symbolic modeling using VRML format to model a room for MR application. In this model, the descriptor was used to provide information regarding the user and user location in the MR space. Ref. [33] suggested a toolkit for building a large-space MR environment, such as a room or a museum based on a Voronoi spatial analysis structure. In this toolkit, virtual scenes are built in the form of a two-dimensional (2D) map; a 3D scene was also constructed using sweeping analysis. Then, arranging virtual objects in a real scene is performed using transformation between the real and virtual spaces. At this step,  
Figure 5: Mixed reality algorithm.  
the toolkit uses anchor data located in the environment to reduce the error of matching the virtual model and the reality. It also eventually uses a Voronoi-based structure to enable the management of editing paths, adjusting the virtual object layer and fast rendering visibility preservation for the user. Ref. [34] proposed a method based on machine learning and Delaunay triangulation to create a mixed reality application for storytelling. In this method, the MR environment was created using the 3D data obtained via scanning and using a specified rule. For this purpose, the mesh was first calculated for the 3D data obtained from the scan, and resampling was used for error reduction. Continuing with the region classification, the previous data were subdivided into a region based on the use of a normal surface vector with a specified threshold limit, which was then used to generate a plane for region subdivision using the Delaunay and RANSAC algorithms. Finally, the model used specific rules such as type, area, and height for the generated data to place scene objects in the classified region. Ref. [35] used a point-primitive and elastic theory model to simulate the deformation of soft tissue for a surgical application.  
Despite the great benefits of the proposed models, there are various challenges that still need to be addressed. In modeling a large space, one needs to consider sharing between different coordinate systems using standard schema such as ontologies, the possibility of using an authoring system, which enables combing, raster, and vector in 2D and 3D using appropriate tracking systems to determine the status of objects with sensor fusion [32]. In MR environment reconstruction using 3D data and machine learning algorithms, it is important to use the appropriate setting of the model, such as the appropriate threshold, and model parameters such as size, etc. [34]. In a simulation model, increasing the output and rendering it at a higher speed requires optimization and computational techniques such as GPU processing [35].