# Realistic Texture Mapping of 3D Medical Models Using RGBD Camera for Mixed Reality Applications
## 4 Discussions  
The first part of this study focused on the registration process between two pointclouds: the first, created using an Intel D435 3D camera, aimed to three-dimensionally reconstruct the external texture of the human head phantom used in this study, while the second, generated from DICOM images obtained from a CBCT scan of the same phantom, aimed to three-dimensionally reconstruct its internal structure. Using bounding boxes to perform an initial alignment between the two models and the iterative algorithm for the refinement appears effective. Figure 11 confirm successful alignment and the quantitative results in Table 1 provide additional validation. The stable registration error along the three principal axes (\(x\), \(y\), and \(z\)) indicates the reliability of the proposed method. Additionally, values around 1 mm in both the mean and standard deviation of the registration error suggest consistent alignment performance, which is critical for correct and faithful texturing of a 3D model, especially for medical or surgical fields. The heatmap represented in Figure 14 shows how the registration error between the RGBD and the DICOM-based pointcloud was spatially distributed, at the end of the registration phases, on the RGBD pointcloud. It can be observed how the pointcloud is composed overwhelmingly of correctly registered points (red points), but also how there are areas, especially concerning the nose, ears, and the surrounding area, where the registration error tends to increase, reaching values even higher than 5 mm (green points). The presence of these areas could be due to an incorrect estimation of the spatial position of the points by the RGBD camera. In particular, this could be caused by both the phantom used that, presenting a very homogeneous external texture, can cause an incorrect triangulation of the points by the 3D camera, and by possible light reflections on the phantom during the acquisition and registration phase of the RGBD pointclouds. Nevertheless, the registration error statistics shown in Figure 14 demonstrate that the majority of points are very close to the DICOM-based pointcloud, resulting in an average registration error tending to around 1 mm but characterized by standard deviation values always greater than the mean (see Table 1). Finally, the values reported in the table, averaging 1 mm, align with other studies where RGBD systems were tested. A study by [PERSON] et al. [48] utilized a Microsoft Kinect device to gather RGBD data, aiming to enhance a CT model for forensic medicine. Their method of acquiring RGBD pointclouds mirrored the approach described in this study, involving manual movement of the Kinect around the target. The mean target registration error was measured at 4.4 \(\pm\) 1.3 mm. Additionally, studies where the RGBD system was rigidly mounted to the CBCT machine [32; 49] were conducted. The  
Figure 15: Different types of object manipulation and interactions using hand gestures. The white lines represent the user’s hand raycasts and allow HoloLens 2 to interact with the digital object when the user’s hands are not in direct contact with the digital object. (**a**) One-handed interaction with the digital object: the user can rotate and translate the object using the corresponding hand movement; (**b**) One-handed interaction with the digital object: the user can rotate the object by selecting one side of the box and then moving its hand; (**c**) One-handed interaction with the digital object: the user can scale the object by selecting one vertex of the box and then moving its hand; (**d**) Two-handed interaction with the digital object: the user can translate and scale the object using intuitive hand movements. first study employed an Intel RealSense F200 RGBD camera to introduce a calibration method based on the simultaneous reconstruction of an object's surface and CBCT scan, achieving an accuracy of 2.58 mm.