Mesh Denoising using Extended ROF Model with L1 Fidelity

dc.contributor.authorWu, Xiaoqunen_US
dc.contributor.authorZheng, Jianminen_US
dc.contributor.authorCai, Yiyuen_US
dc.contributor.authorFu, Chi-Wingen_US
dc.contributor.editorStam, Jos and Mitra, Niloy J. and Xu, Kunen_US
dc.date.accessioned2015-10-07T05:11:56Z
dc.date.available2015-10-07T05:11:56Z
dc.date.issued2015en_US
dc.description.abstractThis paper presents a variational algorithm for feature-preserved mesh denoising. At the heart of the algorithm is a novel variational model composed of three components: fidelity, regularization and fairness, which are specifically designed to have their intuitive roles. In particular, the fidelity is formulated as an L1 data term, which makes the regularization process be less dependent on the exact value of outliers and noise. The regularization is formulated as the total absolute edge-lengthed supplementary angle of the dihedral angle, making the model capable of reconstructing meshes with sharp features. In addition, an augmented Lagrange method is provided to efficiently solve the proposed variational model. Compared to the prior art, the new algorithm has crucial advantages in handling large scale noise, noise along random directions, and different kinds of noise, including random impulsive noise, even in the presence of sharp features. Both visual and quantitative evaluation demonstrates the superiority of the new algorithm.en_US
dc.description.number7en_US
dc.description.sectionheadersShape and Meshen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume34en_US
dc.identifier.doi10.1111/cgf.12743en_US
dc.identifier.pages035-045en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12743en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.5 [Computer Graphics]en_US
dc.subjectComputational Geometry and Object Modelingen_US
dc.subjectGeometric algorithmsen_US
dc.subjectlanguagesen_US
dc.subjectand systemsen_US
dc.titleMesh Denoising using Extended ROF Model with L1 Fidelityen_US
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