## 3 Results
### Test in a Real Setting  
Testing in a real setting was realized by working in a relative frame and using three row of cameras. Due to the practical considerations involving the repositioning of three connected cameras, we added three additional cameras. While theoretically 24 cameras would have been sufficient, the 27-camera network can be seen as a logical extension of our simulated configuration. Minor adjustments to the camera angles, from 25\({}^{\circ}\) to 21\({}^{\circ}\) (9 cameras in a row), had a negligible effect on the overall result of the virtual tests. We conducted these tests in a real environment using the Raspberry Pi High-Quality Camera (Raspberry HQ), a low-cost camera with a 1/2.3\({}^{\prime\prime}\) sensor (Figure 11). The Raspberry HQ [17], based on the Sony IMX477R sensor, has a resolution of 12.3 Megapixel (4056 \(\times\) 3040). The sensor has a width of 6.287 mm and a pixel dimension of 1.55 \(\upmu\)m. Thanks to the global shutter it can also operate with shorter exposure times down to 30 \(\upmu\)s given enough light. Unlike previous models of Raspberry Camera models (Module V1, Module V2, Module 3, and Module 3 wide), the Raspberry HQ is compatible with C-Mount and CS-Mount lens. For our test, we selected the CHIOPT fixed lens model FA1610A with a 16 mm focal length. This setup provides a field of view of approximately 22\({}^{\circ}\) and a ground pixel size of 0.145 mm at a distance of 1.5 m. The camera's compact dimensions (38 mm \(\times\) 8 mm \(\times\) 18.4 mm) facilitate its integration into an engineered system. The Raspberry HQ connects to the Raspberry Pi via a 15 pins MIPI CSI serial interface specifically designed for the camera connection.  
Figure 11: Raspberry Pi, the HQ Camera, and the CHIOPT lens.  
Figure 10: Error distribution in the range from \(-\)0.5 mm to 0.5 mm for the following processing: using the project orientation parameter (yellow); using estimated camera parameters and the 8-camera configuration (blue); using estimated camera parameters and 24-camera configuration (red); working in a relative frame and a 24-camera configuration (black).  
The cameras are synchronized via GPIO ports, using a script that captures the images and saves them to external memory. This system can support multiple Raspberry Pi cameras. We created an environment that closely approximates the virtual one.  
Figure 12 shows the three synchronized cameras in an environment with an area of 1.5 m \(\times\) 2.6 m. Behind the object, we placed a surface with dots at a distance of 7 cm to extract the known distances used to rescale the point cloud. The cameras were arranged in a circular pattern at a distance of 1.5 m from the object. The three rows of acquisition were spaced 0.5 m apart, closely following the virtual configuration. To conduct the tests, the three synchronized cameras were moved to nine different positions: three positions for each camera row (two lateral and one frontal). The head of the object was positioned at the height of the middle row. Such small sensors are more sensitive to low light conditions. Therefore, we used a professional umbrella setup behind the cameras, pointed upward, using cold light. The series of tests included the reconstruction of a 3D-printed 3D model, which allowed for the quantitative analysis of the reconstruction errors, as well as the reconstruction of three real individuals who were able to maintain their position for the duration of the nine shots.