## 2 Analysis of Existing Technology Solutions
### Methods, Tools, and Techniques  
Approaches to the 3D reconstruction of the human foot based on computer vision algorithms can be divided into three types: the use of special sensors, classical stereometry algorithms, and machine-learning methods. The authors of [11] suggest using the Microsoft Kinect device [12; 13]. Special construction is used to automate the process. This construction consists of a small table and an engine that rotates the device around the human foot. The scanning angle, in this case, is 270 degrees. To obtain a series of depth images, a point cloud is constructed, which is then filtered using the iterative closest point (ICP) algorithm [14]. Finally, a 3D model of the foot is generated. The authors state that the use of this method makes it possible to achieve a maximum deviation of 0.85 mm in linear measurements while reconstructing the lower arch. Other works [15; 16] use modern portable IntelRealsense depth scanners. Researchers of the method used in [17] assume that for a full-fledged reconstruction, including a lower arch, it is necessary to use four angles with a difference of 90 degrees. These four depth images are used for several transformations involving merging, point cloud building, and smoothing. After this post-processing, the extra reconstructed objects are performed. Thus, the deviation of length and width measurements is 0.355 mm. The authors of the method in [18] suggest using smartphones with integrated depth sensors (LiDAR, Face ID, etc.) to obtain a 3D model. The authors used the PCA method to establish the final 3D model, in addition to the point cloud obtained by a circular foot survey. The main idea of the approach is to create a deformable model based on 63 sets of feet. Applying the PCA method minimizes model shape error. Based on the 12 parameters extracted by the authors, the researchers achieved a coverage of 93% of the manually measured parameters. RANSAC is used as a segmentation algorithm, which works with a point cloud and is known as a stable method for estimating model parameters based on random samples. The experiments showed that the presented method has a deviation of 1.13 mm. The developers of the Sensock method [19] used their own sensor, which consists of four stretchable sensors made of silk fibrin threads. The authors determined four characteristic girths of the foot based on the available knowledge of anatomy and measured their length using the resistance value of the stretched sensors. Based on these extracted parameters, a 3D model can be constructed. As an experiment, the researchers recruited 15 men and 10 women and used their feet for 10 sessions of reconstruction. The authors used standard deviation to estimate the error on the studied parameters; in the case of linear measurements, the average deviation was 1.01 mm. The advantages of using special sensors include high accuracy compared to other methods (minimal deviation) and the quickest time creating 3D models. The disadvantages are the high cost of the sensors and smartphones that support laser scanning. In [20], the approach and methods of photogrammetry [21; 22] were used. By performing serial circular photography with a smartphone, the authors used the feature-extraction and -matching algorithm SIFT [23]. The essence of SIFT is to form a set of feature descriptors on the images. Three-dimensional space is formed based on extracted and matched features, which then allows for the construction of a sparse point cloud (Structure-from-Motion). After, the RANSAC algorithm selects points that are connected to each other and removes unconnected points. After these stages, a dense point cloud model is constructed using the PMVS tool [24]. Based on this model, polygons for the 3D shape of the human foot are generated, and textures are constructed. Post-processing of the 3D model includes the removal of unnecessary reconstructed objects and backgrounds.