Mesh Density Adaptation for Template-based Shape Reconstruction
This addresses a specific issue in 3D shape reconstruction for computer vision and graphics applications, but it is incremental as it builds on existing regularization methods.
The paper tackles the problem of under-sampling near shape details in template-based 3D shape reconstruction by proposing a mesh density adaptation method, resulting in more accurate reconstructions as demonstrated in inverse rendering and non-rigid surface registration tasks.
In 3D shape reconstruction based on template mesh deformation, a regularization, such as smoothness energy, is employed to guide the reconstruction into a desirable direction. In this paper, we highlight an often overlooked property in the regularization: the vertex density in the mesh. Without careful control on the density, the reconstruction may suffer from under-sampling of vertices near shape details. We propose a novel mesh density adaptation method to resolve the under-sampling problem. Our mesh density adaptation energy increases the density of vertices near complex structures via deformation to help reconstruction of shape details. We demonstrate the usability and performance of mesh density adaptation with two tasks, inverse rendering and non-rigid surface registration. Our method produces more accurate reconstruction results compared to the cases without mesh density adaptation.