In certain embodiments, as described herein, an output feature vector can be generated that would include the horizontal and vertical coordinates (i.e., the 2D coordinate pair) of the 2D matrix point (X17, Y3) and the determined depth coordinate (z-value) of the 3D point 464 The output feature vector can also include the distance value 470.
Although the 2D image matrix 402, raw 3D image 404, point cloud 406, and other items of FIG. 4, are shown in perspective view in a 3D environment, FIG. 4 can represent a visualization of data structures and information generated or otherwise analyzed by, for example, a computing device, such as any of the computing devices of FIG. 1 or 2. The items of FIG. 4, such as the 2D image matrix 402, 2D matrix points (430, 434), point cloud 406, 3D points (460-464), may be represented in the computing device, such as within the computing's memory, in various data structures including, for example, a data table, matrix, grid, array, multiple dimension array, hash, “struct,” dictionary, vector, or any other data structure that may be used to arrange or organize the items of FIG. 4 in computer memory. Such data structures may be implemented in a variety of computer languages, for example, Python, Java, C++, C#, R or similar languages.
