Deep Mesh Prior: Unsupervised Mesh Restoration using Graph Convolutional Networks

• URL: http://arxiv.org/abs/2107.02909v1
• Date: Fri, 2 Jul 2021 07:21:10 GMT
• Title: Deep Mesh Prior: Unsupervised Mesh Restoration using Graph Convolutional Networks
• Authors: Shota Hattori, Tatsuya Yatagawa, Yutaka Ohtake, Hiromasa Suzuki
• Abstract summary: We propose a graph convolutional network on meshes to learn self-similarity. The network takes a single incomplete mesh as input data and directly outputs the reconstructed mesh. We demonstrate that our unsupervised method performs equally well or even better than the state-of-the-art methods using large-scale datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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