FIG. 5B depicts the same view of the 3D visualization 500 of the 3D image of FIG. 5A, but also incorporates a generated 2D image matrix 560 mapped to the 3D image. The 2D image matrix 560 may be generated by the Distify method as described for FIGS. 3 and 4 herein. For example, the 2D image matrix 560 can correspond to the 2D image matrix 402 of FIG. 4, and, therefore, in some cases, the related disclosure with respect to the 2D image matrix 402 applies similarly with respect to 2D image matrix 560. Accordingly, the 2D image matrix 560 can be used to normalize a 3D point cloud associated with the 3D image of 3D visualization 500. For example, 3D point 506 (related to the driver's forehead) can be mapped directly to a 2D matrix point of 2D image matrix 560. In contrast, 3D point 504 (related to the driver's cheek or lip area) is not mapped directly to a 2D matrix point of the 2D image matrix 560, such that 3D point 504 could correspond to 3D point 464 of FIG. 4. Thus, as described for FIG. 4, the Distification method can associate the depth coordinate (e.g., z-value) of 3D point 506 with its directly mapped 2D matrix point because the horizontal and vertical coordinate pairs of both points would be the same, and, therefore would be the “nearest” points with respect to one another. 3D point 504, however, is not directly mapped to a particular 2D matrix point of the 2D image matrix 560. Thus, the Distification method could determine the nearest 2D matrix point for a 3D point as described, for example, for 3D point 464 of FIG. 4.
