Deep Mesh Prior: Unsupervised Mesh Restoration using Graph Convolutional Networks

Autor:	Hattori, Shota, Yatagawa, Tatsuya, Ohtake, Yutaka, Suzuki, Hiromasa
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Graphics
Druh dokumentu:	Working Paper
Popis:	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. Comment: 10 pages, 9 figures and 2 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2107.02909 Zobrazit plný text záznamu View this record from Arxiv