Learning Self-Prior for Mesh Inpainting Using Self-Supervised Graph Convolutional Networks
Shota Hattori,
Tatsuya Yatagawa,
Yutaka Ohtake
et al.
Abstract:In this paper, we present a self-prior-based mesh inpainting framework that requires only an incomplete mesh as input, without the need for any training datasets. Additionally, our method maintains the polygonal mesh format throughout the inpainting process without converting the shape format to an intermediate one, such as a voxel grid, a point cloud, or an implicit function, which are typically considered easier for deep neural networks to process. To achieve this goal, we introduce two graph convolutional n… Show more
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