This study presents 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, 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 networks (GCNs):
single-resolution GCN (SGCN) and multi-resolution GCN (MGCN), both trained in a
self-supervised manner. Our approach refines a watertight mesh obtained from
the initial hole filling to generate a completed output mesh. Specifically, we
train the GCNs to deform an oversmoothed version of the input mesh into the
expected completed shape. To supervise the GCNs for accurate vertex
displacements, despite the unknown correct displacements at real holes, we
utilize multiple sets of meshes with several connected regions marked as fake
holes. The correct displacements are known for vertices in these fake holes,
enabling network training with loss functions that assess the accuracy of
displacement vectors estimated by the GCNs. We demonstrate that our method
outperforms traditional dataset-independent approaches and exhibits greater
robustness compared to other deep-learning-based methods for shapes that less
frequently appear in shape datasets.

本研究提出一种自我先验的网格修补框架，通过引入两个自监督图卷积网络训练来实现，从而在不需要任何训练数据集的情况下只需一个不完整的网格作为输入，并在修补过程中保持原始多边形网格格式，最终可以生成一个修补好的多边形网格。