In this Chapter, we propose different data-driven parameterization procedures depending on the nature of the input data, whether they consist of point sequences or point clouds, as well as whether they are organized or scattered. CNN are employed for the parameterization learning problem of points on a rectilinear grid; on the other hand, we propose to employ methods from geometric deep learning to properly address the parameterization learning problem for unstructured data configurations.