## 4 Experiments
### Extracting Foot Parameters  
To evaluate the quality of reconstructed 3D models by foot length and width, the Grand control point method was used. This approach allows converting point cloud model coordinates to world coordinates using the actual size. Thus, in the process of generating a dataset for 3D modeling, a coin with known real-world readings was attached to the bottom of the volunteers' feet. This is necessary to calibrate and establish the ratio between the 3D coordinate system and the world coordinate system. Since it is rather difficult to automate such an evaluation process, special tools in the MeshLab software were used to measure the distance between points. Equations (9) and (10) were used to calculate the parameters of length (\(FL\)) and width (\(FW\)) of the foot. The left part of the formulas shows the measurements obtained manually with a caliper, and the right part shows the distances between the points obtained by repeated measurements and averaging. \[\frac{FL(worldsize)}{CL(worldsize)} =\frac{FL(3 Dmodelsize)}{CL(3 Dmodelsize)} \tag{9}\] \[\frac{FW(worldsize)}{CW(worldsize)} =\frac{FW(3 Dmodelsize)}{CW(3 Dmodelsize)} \tag{10}\]  
Based on the presented formulas, with data on the length and width of the coin in the real world, it is possible to express the parameters of the foot. Formulas (11) and (12) demonstrate the calculation of the length and width of the foot, respectively. \[FL(worldsize)=\frac{FL(3 Dmodelsize)}{CL(3 Dmodelsize)}*CL(worldsize) \tag{11}\] \[FW(worldsize)=\frac{FW(3 Dmodelsize)}{CW(3 Dmodelsize)}*CW(worldsize) \tag{12}\]  
Figure 7: Segmented foot with the Mask R-CNN model. Investigation of the Influence of Different Algorithms for Extracting and Comparing Informative Features on the Quality of 3D Models  
The key point that affects the quality of 3D foot models is the correct selection of algorithms for feature extraction and matching. The SIFT, DSP-SIFT, and AKAZE algorithms were proposed earlier in the described methodology. Table 3 demonstrates the effect of combinations of algorithms on length (_FL_) and width (_FW_) deviations relative to manual measurements for 10 subjects. The limit for the number of extracted features was set to 2000 per image. The created depth maps were scaled down two times relative to the original image. Additionally presented is the average time to create a 3D model for 100 images in 1920 \(\times\) 1080 (pixels) format and the standard deviations for the samples. Figure 8 shows an example of a reconstructed 3D model with a resolution of \(1920\times 1080\) (pixels) of a series of images. Thus, the range of length measurements on a sample of 10 volunteers was varied from 235 to 285 mm, and that of the width measurements from 91 to 127 mm. The best standard deviation of 0.87 mm was obtained with the combination of the SIFT, AKAZE, and DSP-SIFT feature-extraction algorithms, and the average time to create one model, including series generation and image segmentation, was 11 min 29 s.  
Figure 8: Demonstration of a reconstructed 3D model of the foot based on a series of images with resolution of 1920 \(\times\) 1080 (pixels). (**a**) Left side of the foot; (**b**) Right side of the foot.