  We propose a totally functional view of geometric matrix completion problem.
Differently from existing work, we propose a novel regularization inspired from
the functional map literature that is more interpretable and theoretically
sound. On synthetic tasks with strong underlying geometric structure, our
framework outperforms state of the art by a huge margin (two order of
magnitude) demonstrating the potential of our approach. On real datasets, we
achieve state-of-the-art results at a fraction of the computational effort of
previous methods. Our code is publicly available at
https://github.com/Not-IITian/functional-matrix-completion
