Mesh denoising has been widely used for geometry modeling and processing. Before raw noisy 3D models, obtained by various methods including scanners or vision-based 3D reconstruction, can be practically used for a variety of geometry processing, animation, and rendering applications, we need to produce their cleaned versions via mesh denoising. The main technical bottleneck of designing robust mesh denoising algorithms is to automatically identify various geometric features of interest (i.e., sharp and shallow features) on noisy models. Furthermore, this issue will become more challenging when heavy noise is present and/or input meshes are irregularly sampled.
The general process of a mesh denoising approach.
From left to right: initial surface, surface corrupted by Gaussian noise in random directions with standard deviation σ = 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. This figure is extracted from [He and Schafer 2013].
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