Instead of going from a set of parameters to a shape, we can go from a shape to the parameters which best describe that shape. As different example of analysis, we can build the model shown in figure 2. If we then ask the model, without any training or labeling of the shapes in the model, to compare the similarity of the shapes, we get the matrix shown graphically in figure 3. Thus, through unsupervised learning, we have found a distinction between cats and dogs. The matrix of shape correlation in the model.
Red values indicate positive correlation and blue values indicate negative correlation.
In particular, it codes which shapes belong to the class and which do not. This can be used as a constraint for other algorithms. 3D reconstruction often results in noisy shapes since it is unclear which areas of the object are actually sharp and which appear to be so due to noise in the inputs. The model can be used to add exactly this kind of knowledge as a constraint on the resulting shape. This allows better reconstructions using fewer views of the object.