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arXiv · 2107.02909

Deep Mesh Prior: Unsupervised Mesh Restoration using Graph Convolutional Networks

Shota Hattori·Tatsuya Yatagawa·Yutaka Ohtake·Hiromasa Suzuki
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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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Shota Hattori, Tatsuya Yatagawa, Yutaka Ohtake, Hiromasa Suzuki. 2021-07-02. Deep Mesh Prior: Unsupervised Mesh Restoration using Graph Convolutional Networks. https://arxiv.org/abs/2107.02909

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