Summary: 1. predict depth from a single image with an accuracy of 100% (Andrew Ng and Saxena have done this with an accuracy of 67%), then, 2. use a training set of real 3d models to calculate voxels outside of a test 3d model (ideally one generated by the first project) (first predicting a single voxel, then adding that voxel to the set of known voxels, then using the new set of voxels to predict another voxel, and so on).<p>Will this generate the desired results (being able to calculate voxels outside of a given 3d model, and ultimately, allowing us to see things outside of the original camera scene of a given photo)?