Patch-based mesh inpainting via low rank recovery

Mesh inpainting aims to fill the holes or missing regions from observed incomplete meshes and keep consistent with prior knowledge. Inspired by the success of low rank in describing similarity, we formulate the mesh inpainting problem as the low rank matrix recovery problem and present a patch-based mesh inpainting algorithm. Normal patch covariance is adapted to describe the similarity between surface patches. By analyzing the similarity of patches, the most similar patches are packed into a matrix with low rank structure. An iterative diffusion strategy is first designed to recover the patch vertex normals gradually. Then, the normals are refined by low rank approximation to keep the overall consistency and vertex positions are finally updated. We conduct several experiments in different 3D models to verify the proposed approach. Compared with existing algorithms, our experimental results demonstrate the superiority of our approach both visually and quantitatively in recovering the mesh with self-similarity patterns.