Robust and effective mesh denoising using L0 sparse regularization. (August 2018)

• Record Type: Journal Article
• Authors: Zhao, Yong; Qin, Hong; Zeng, Xueying; Xu, Junli; Dong, Junyu
• Is Part Of: Computer aided design. Volume 101(2018)
• Page Start: 82
• Page End: 97
• Publication Date: 2018-08
• Subjects: Mesh denoising -- L0 norm -- Sparse regularization -- Non-convex optimization -- Multi-layer approach; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2018.04.001
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: Mesh denoising is of great practical importance in geometric analysis and processing. In this paper we develop a novel L 0 sparse regularization method to robustly and reliably eliminate noises while preserving features with theoretic guarantee, and our assumption is that, local regions of a noise-free shape should be smooth unless they contain geometric features. Both vertex positions and facet normals are integrated into the L 0 norm to measure the sparsity of geometric features, and are then optimized in a sparsity-controllable fashion. We design an improved alternating optimization strategy to solve the L 0 minimization problem, which is proved to be both convergent and stable. As a result, our sparse regularization exhibits its advantage to distinguish features from noises. To further improve the computational performance, we propose a multi-layer approach based on joint bilateral upsampling to handle large and complicated meshes. Moreover, the aforementioned framework is naturally accommodating the need of denoising time-varying mesh sequences. Both theoretical analysis and various experimental results on synthetic and natural noises have demonstrated that, our method can robustly recover multifarious features and smooth regions of 3D shapes even with severe noise corruption, and outperform the state-of-the-art methods. Highlights: A novel L 0 sparse regularization for noise elimination and feature preservation. An improved optimization strategy with guaranteed convergence and stability. A new multi-layer approach for performance improvement.