Mesh Denoising of Developable Surfaces with Curved Foldings

This paper presents a novel mesh denoising approach designed specifically for developable models with curved folds, going beyond traditional denoising metrics to focus on restoring the model's developability. We introduce a metric based on normal variation to assess mesh developability and integrate it into an optimization problem that aims to increase the sparsity of the normal vector field, leading to a dedicated mesh denoising algorithm. The performance of our method is evaluated across a wide range of criteria, including standard metrics and surface developability determined through Gaussian curvature. Through testing on a variety of noisy models and comparison with several state-of-the-art mesh denoising and developability optimization techniques, our approach demonstrates superior performance in both traditional metrics and the enhancement of mesh developability.