Mesh Denoising with (Geo)Metric Fidelity.

Abstract

Working with noisy meshes and aiming at providing high-fidelity 3D object models without tampering the metric quality of the acquisitions, we propose a mesh denoising technique that, through a normal-diffusion process guided by a curvature saliency map, is able to preserve and emphasize the natural object features, concurrently allowing the introduction of a bound on the maximum distance from the original model. Moreover, both the position of the mesh vertices and the edge orientations are optimized through a tailored geometric-aliasing correction. Thanks to an efficiently parallelized procedure, we are able to process even large models almost instantly with a parameter configuration that does not depend on the scale of the object. An essential survey on mesh denoising is also presented which is functional to the definition of a common framework where to set up our solutions and the related technical and experimental comparisons. The proposed results prove the effectiveness of our method, especially on the challenging target application profiles. Where competing techniques tend to inappropriately recover sharp edges while deforming the surrounding geometry or, on the contrary, to oversmooth shallow features, our method protects and enhances the natural object features and effectively reduces scanning noise on the smooth parts, while guaranteeing the prescribed metric-fidelity to the input model.