Tensor Voting Guided Mesh Denoising

TLDR: This work votes on surface normal tensors from robust statistics to guide the creation of consistent subneighborhoods subsequently used by moving least squares (MLS) to give a unified mesh-denoising framework for not only handling noise but also enabling the recovering of surfaces with both sharp and small-scale features.

Abstract: Mesh denoising is imperative for improving imperfect surfaces acquired by scanning devices The main challenge is to faithfully retain geometric features and avoid introducing additional artifacts when removing noise Unlike the existing mesh denoising techniques that focus only on either the first-order features or high-order differential properties, our approach exploits the synergy when facet normals and quadric surfaces are integrated to recover a piecewise smooth surface In specific, we vote on surface normal tensors from robust statistics to guide the creation of consistent subneighborhoods subsequently used by moving least squares (MLS) This voting naturally leads to a conceptually simple way that gives a unified mesh-denoising framework for not only handling noise but also enabling the recovering of surfaces with both sharp and small-scale features The effectiveness of our framework stems from: 1) the multiscale tensor voting that avoids the influence from noise; 2) the effective energy minimization strategy to searching the consistent subneighborhoods; and 3) the piecewise MLS that fully prevents the side effects from different subneighborhoods during surface fitting Our framework is direct, practical, and easy to understand Comparisons with the state-of-the-art methods demonstrate its outstanding performance on feature preservation and artifact suppression