[D] Is accurately estimating the contents of a voxel outside of a real 3D model possible?
Imagine we have a training set of real 3D models (by this I mean, for example, 3D models of a rooms in buildings), and with this training set, we train a neural network to understand the relationship between a voxel and the 3D model in which it resides (I hypothesize that something like light would be a factor). Then, we use our neural network to predict one voxel outside of the captured (by a camera) 3D model. Is this possible? Would the desired results be achieved?