Geometry Denoising with Preferred Normal Vectors
This addresses geometry denoising for computer graphics or vision applications, but it appears incremental as it builds on existing regularization and optimization techniques.
The paper tackles geometry denoising by incorporating prior knowledge of preferred normal vectors, resulting in a method that embeds segmentation into the denoising process and uses a split Bregman approach for optimization.
We introduce a new paradigm for geometry denoising using prior knowledge about the surface normal vector. This prior knowledge comes in the form of a set of preferred normal vectors, which we refer to as label vectors. A segmentation problem is naturally embedded in the denoising process. The segmentation is based on the similarity of the normal vector to the elements of the set of label vectors. Regularization is achieved by a total variation term. We formulate a split Bregman (ADMM) approach to solve the resulting optimization problem. The vertex update step is based on second-order shape calculus.