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Mesh Denoising via L0 Minimization

By Lei He and Scott Schaefer


Figure 1: From left to right: initial surface, surface corrupted by Gaussian noise in random directions with standard deviationσ = 0.4le (le is the mean edge length), bilateral filtering [Fleishman et al. 2003], prescribed mean curvature flow [Hildebrandt and Polthier 2004], mean filtering [Yagou et al. 2002], bilateral normal filtering [Zheng et al. 2011], our method. The wireframe shows folded triangles as red edges. We present an algorithm for denoising triangulated models based on L0 minimization. Our method maximizes the flat regions of the model and gradually removes noise while preserving sharp features. As part of this process, we build a discrete differential operator for arbitrary triangle meshes that is robust with respect to degenerate triangulations. We compare our method versus other anisotropic denoising algorithms and demonstrate that our method is more robust and produces good results even in the presence of high noise

Topics: mesh denoising, L0 minimization Links, DL PDF
Year: 2013
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