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Erschienen in:
2016 | OriginalPaper | Buchkapitel
11. Regularized 3D Image Smoothing
verfasst von : Mongi A. Abidi, Andrei V. Gribok, Joonki Paik
Erschienen in: Optimization Techniques in Computer Vision
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Abstract
This chapter discusses a new surface smoothing method based on area decreasing flow, which can be used for preprocessing raw range data or postprocessing reconstructed surfaces. Although surface area minimization is mathematically equivalent to the mean curvature flow, area decreasing flow is far more efficient for smoothing the discrete surface on which the mean curvature is difficult to estimate. A general framework of regularization based on area decreasing flow is proposed and applied to smoothing range data and arbitrary triangle mesh. Crease edges are preserved by adaptively changing the regularization parameter. The edge strength of each vertex on a triangle mesh is computed by fusing the tensor voting and the orientation check of the normal vector field inside a geodesic window. Experimental results show that the proposed algorithm provides successful smoothing for both raw range data and surface meshes.