Random walks for feature-preserving mesh denoising
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Cited by (74)
A fast, efficient, and explicit phase-field model for 3D mesh denoising
2023, Applied Mathematics and ComputationAdaptive and propagated mesh filtering
2023, CAD Computer Aided DesignHLO: Half-kernel Laplacian operator for surface smoothing
2020, CAD Computer Aided DesignFeature-convinced mesh denoising
2019, Graphical ModelsPropagated mesh normal filtering
2018, Computers and Graphics (Pergamon)Citation Excerpt :The basic idea is calculating the weight based on the similarity between local neighborhoods of the face being processed and the other faces. Instead of updating the vertex positions directly, the approach of filtering face normals firstly and then updating vertex positions is adopted by more and more mesh filters [1,5,22,23]. The main difference among these methods is their normal filtering strategies.
Robust and effective mesh denoising using L<inf>0</inf> sparse regularization
2018, CAD Computer Aided DesignCitation Excerpt :Shen et al. [30] introduced a fuzzy vector median filter. When calculating local weights, Sun et al. [5] assigned null weight to neighboring facet normals with larger variation to retain sharp edges, and later they used random walks [31]. Zheng et al. [7] employed bilateral filter to better smooth facet normals.
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