Feature-Aware Trilateral Filter With Energy Minimization for 3D Mesh Denoising

Abstract

Sharp edges and corners are crucial features for high-quality and fine-detailed 3D meshes, which we tend to treat as noises in previous tasks of mesh denoising mistakenly. A challenge arises on how to handle both surfaces and features simultaneously in 3D mesh denoising. Classical works mainly focused on surfaces, whereas features also reasonably need proper processes. In this paper, we propose a feature-aware trilateral filter under the framework of energy minimization for 3D mesh denoising and address the above challenge. Concretely, we treat the challenge as an energy minimization model, where the data and smooth terms are both carefully designed. Apart from this, we introduce a feature-aware trilateral filter for high-quality mesh guidance to the model. In this filter, features are detected and distinguished from surfaces for consequent guidance. With the help of the model and filter, more priors are involved, and we can make global optimization for better denoising performance. We perform experiments on both synthetic and scanned meshes, where both subjective and objective evaluations are displayed to show the superior performance of our method to state-of-the-art methods. Furthermore, two experiments, including ablation tests and parameter sensitivities tests, are conducted to show the robustness and efficiency of our method thoroughly. All these results demonstrate that our method is suitable for robust and efficient performance in future mesh-oriented applications.