  This paper addresses mesh restoration problems, i.e., denoising and
completion, by learning self-similarity in an unsupervised manner. For this
purpose, the proposed method, which we refer to as Deep Mesh Prior, uses a
graph convolutional network on meshes to learn the self-similarity. The network
takes a single incomplete mesh as input data and directly outputs the
reconstructed mesh without being trained using large-scale datasets. Our method
does not use any intermediate representations such as an implicit field because
the whole process works on a mesh. We demonstrate that our unsupervised method
performs equally well or even better than the state-of-the-art methods using
large-scale datasets.
