## 3 Results  
### Test in a Virtual Environment  
These tests were conducted within the virtual environment of 3 DStudio Max. We modelled a 2.0 m \(\times\) 2.0 m box and positioned the 3D model (in *.obj format) at its centre. Circular targets of 1.5 cm in diameter were inserted along the wall, used as GCPs. The coordinates of the GCPs established the world frame, with the origin situated at the centre of the box, the xy axes aligned with the floor, and the z-axis pointing upwards. Within the software, we configured the virtual camera parameters (focal length, sensor dimension, and resolution) to match those of our Raspberry Pi camera. Images were acquired within 3 DStudioMax. Figure 7 shows the 24 images of the virtual model obtained in 3 DStudio Max. The first row of cameras corresponds to the upwards cameras, the second row corresponds to the frontal cameras, and the last row corresponds to the downward-facing cameras. In the first test, we imported in the Agisoft software the camera orientation parameters set in 3 DStudioMax to acquire the images (true orientation parameters) via the open-source industry standard Alembic (*abc) format. This format allows seamless communication between 3 DStudioMax and Agisoft. Since these parameters were directly imported, reconstruction errors in this test solely depend on the dense matching algorithm and the camera network. We compared the ground truth directly to the resulting point cloud, which was oriented and scaled. No significant difference was observed between using 8 cameras (sqm = 0.037 mm) and 24 cameras (sqm = 0.036 mm). In both cases, the mean error was zero as expected from a normal distribution of the error. Additionally, there was no significant difference in coverage observed between the two configurations. Figure 8 shows the colour map of the signed reconstruction error within \(\pm\)3 standard deviation (\(\sigma\)), superimposed on the 3D model. Reconstruction errors within \(\pm\)0.02 mm are represented in green, errors up to 0.1 mm in red, and errors down to \(-\)0.1 mm are indicated in blue. Figure 8: Reconstruction error superimposed on the 3D model. Error between \(\pm\)0.02 mm represented in green; error up 0.1 mm in red and error down to \(-\)0.1 mm in blue. Figure 7: A total number of 24 frames acquired within 3 DSMax 2022. The reconstruction error ranges from \(-0.2\) mm to \(0.2\) mm depending on noise measurement, and it represent the (ideal) limit we can obtain with our system; they are comparable to the intrinsic error of the 3D printed model, and this is additionally confirms that we can neglect the intrinsic error of the 3D printer as it did not affect our result. The next tests show the influence of the orientation parameter in the 3D reconstruction. The images acquired within 3 DStudioMax (Figure 7) were aligned by Agisoft using a sparse cloud of TPs. We explored the two photogrammetric approaches: working in a relative frame using only TPs and in a real-world frame using both TPs and GCPs. The world-frame test (the frame defined in 3 DStudio Max) allowed us to evaluate both the quality of orientation parameters and the reconstruction errors. An accurate camera orientation is crucial for achieving high-quality reconstructions. To improve the estimation of the camera orientation parameters, we experimented with increasing the number of GCPs. However, we found that this approach did not yield results comparable to those obtained using the relative frame and 24-camera configuration. This observation was supported by the results with true camera orientation parameters, which provide a baseline for understanding the error distribution in a system without the influence of orientation parameter errors, as well as providing reference values for camera orientation. When using eight cameras, we measured positional errors ranging from \(1.0\) to \(1.5\) centimetres for all cameras. This is significantly larger than the 1-2-millimetre errors observed using a triple row of cameras.