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Deep Mesh Prior: Unsupervised Mesh Restoration using Graph Convolutional Networks

2021-07-02 07:21:10
Shota Hattori, Tatsuya Yatagawa, Yutaka Ohtake, Hiromasa Suzuki

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.

Abstract (translated)

URL

https://arxiv.org/abs/2107.02909

PDF

https://arxiv.org/pdf/2107.02909.pdf


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