Sparsity-aided Variational Mesh Restoration

We propose a variational method for recovering discrete sur- faces from noisy observations which promotes sparsity in the normal vari- ation more accurately than `1 norm (total variation) and `0 pseudo-norm regularization methods by incorporating a parameterized non-convex penalty function. This results in denoised surfaces with enhanced at regions and maximally preserved sharp features, including edges and corners. Unlike the classical two-steps mesh denoising approaches, we propose a unique, eective optimization model which is eciently solved by an instance of Alternating Direction Method of Multipliers. Experi- ments are presented which strongly indicate that using the sparsity-aided formulation holds the potential for accurate restorations even in the pres- ence of high noise.