Geometric surface processing via normal maps
We propose that the generalization of signal and image processing to surfaces entails filtering the normals of the surface, rather than filtering the positions of points on a mesh. Using a variational strategy, penalty functions on the surface geometry can be formulated as penalty functions on the surface normals, which are computed using geometry-based shape metrics and minimized using fourth-order gradient descent partial differential equations (PDEs). In this paper, we introduce a two-step approach to implementing geometric processing tools for surfaces: (i) operating on the normal map of a surface, and (ii) manipulating the surface to fit the processed normals. Iterating this two-step process, we efficiently can implement geometric fourth-order flows by solving a set of coupled second-order PDEs. The computational approach uses level set surface models; therefore, the processing does not depend on any underlying parameterization. This paper will demonstrate that the proposed strategy provides for a wide range of surface processing operations, including edge-preserving smoothing and high-boost filtering. Furthermore, the generality of the implementation makes it appropriate for very complex surface models, for example, those constructed directly from measured data.
geometric surface processing level sets high-boost filtering algorithms theory surface fairing anisotropic diffusion