## 3 Results
### Scan Result Analysis
These methods must be designed specifically for the proposed prototype device and with respect to the actual proposed program function. One approach involves modifying the custom function to calculate the arithmetic mean of the non-zero values (i.e., the laser beam) of the binary image in the same line. The center image point is then utilized to facilitate a comparison with the center image point calculated in the same manner on the other binary image. This approach eliminates the necessity for performing multiple disparity calculations within a single row of image points, thereby streamlining the process. This approach is predicated on the hypothesis that within a specific thickness of the laser beam image points in a single row, the most accurate 3D coordinate point can be obtained if only the arithmetic center is taken into account in the calculation. To this end, the np.unique function is employed, enabling the execution of the calculation for each row individually by generating a distinct list of rows. This list is then utilized for the calculation of the center points. Subsequently, the calculation of the 3D coordinate points is performed. The second method is similarly based on a modification of the original program function, while the actual calculation of the 3D coordinate points remains unchanged. In this approach, for each distinct row of image points of the binary image, a 3D coordinate point is calculated as the arithmetic mean of the depth in the \(Z\)-axis of the obtained 3D coordinate points in that row. Subsequently, the selected proposed features were tested under the same object digitization scene setup as in the test scan with the original feature. A substantial enhancement in the quality of the point clouds was discernible through a visual examination. As illustrated in Figure 11(a), the point cloud resulting from the digitization using the original program feature can be observed. When viewed from the right, as depicted in Figure 11(d), the layering of the point cloud is clearly apparent. Furthermore, when observing Figure 11(b) from the front and Figure 11(e) from the right, it is evident that employing the modified program function, which calculates the arithmetic mean of the image points in the binary image, results in a substantial enhancement in the quality of the obtained point cloud. The effect of layer formation is noticeably reduced, and in some cases, it is completely eliminated. A similar observation can be made in Figure 11(c) from the front and Figure 11(f) from the right, where digitization is performed using a modified program function based on averaging the acquired 3D coordinate points. These two point clouds exhibit a high degree of similarity after adjusting the parameters with respect to quality, and no significant enhancement in quality is discernible between them, making it challenging to select a suitable one. Consequently, it is imperative to digitize a shapeless complex object and to perform a scan of only one laser projection. In order to select the most appropriate function from the two proposed, scans were performed with only a single laser beam. This is depicted in Figure 13. The computer motherboard, having been digitized in the preceding testing, exhibited a surface of excessively intricate geometry. The scanning of motherboards and electronic components presents a significant challenge, even when employing industrial-grade scanners. Consequently, a cardboard box devoid of any printing was selected for scanning. In the context of scanning the planar surface of the box, it is possible to select a more suitable program function with greater certainty based on the quality of the obtained point cloud. The scanning conditions were designed to mirror prior scans, ensuring consistency and repeatability. As illustrated in Figure 14., the projection of the laser beam onto the planar surface of the cardboard box is clearly discernible within the ESP32-CAM device images. The laser pattern can be unambiguously registered.