Surface mesh denoising with normal tensor framework

In this paper, we propose a novel method for feature-preserving mesh denoising based on the normal tensor framework. We utilize the normal tensor voting directly for the mesh denoising whose eigenvalues and eigenvectors are used for detecting saliency, and introduce an algorithm that updates a vertex by the Laplacian of curvature which minimizes a difference of the curvature in one neighborhood. By connecting the feature saliency with a distance metric in the normal tensor space, our algorithm preserves sharp features more robustly and clearly for noisy mesh data. Comparing our method with the existing ones, we demonstrate the effectiveness of our algorithm against some synthetic noisy data and real-world scanned data.

► We propose a novel method for feature-preserving mesh denoising based on the normal tensor. ► The method preserves sharp features more robustly and clearly for noisy mesh data. ► The method prevents from the shrinkage of Laplacian smoothing. ► The algorithm is free from a cumbersome setting of parameters.