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A study of anisotropic geometric diffusion as a powerful multiscale method for processing textured surfaces is presented. The process is based on an anisotropic curvature evolution of the surface and an anisotropic diffusion on the processed surface applied to the texture, and aims to smooth noisy triangulated surfaces and noisy textures obtained due to the new scanning technology.

The stable local classification of discrete surfaces with respect to features such as edges and corners or concave and convex regions, respectively, is as quite difficult as well as indispensable for many surface processing applications. Usually, the feature detection is done via a local curvature analysis. If concerned with large triangular and irregular grids, e.g., generated via a marching cube algorithm, the detectors are tedious to treat and a robust classification is hard to achieve. Here, a local classification method on surfaces is presented which avoids the evaluation of discretized curvature quantities. Moreover, it provides an indicator for smoothness of a given discrete surface and comes together with a built-in multiscale. The proposed classification tool is based on local zero and first moments on the discrete surface. The corresponding integral quantities are stable to compute and they give less noisy results compared to discrete curvature quantities. The stencil width for the integration of the moments turns out to be the scale parameter. Prospective surface processing applications are the segmentation on surfaces, surface comparison, and matching and surface modeling. Here, a method for feature preserving faring of surfaces is discussed to underline the applicability of the presented approach. [ABSTRACT FROM AUTHOR]