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Propagated mesh normal filtering.

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Bibliographic Details
Title:	Propagated mesh normal filtering.
Authors:	Liu, Bin¹, Cao, Junjie¹, Wang, Weiming¹, Ma, Ning¹, Li, Bo², Liu, Ligang³, Liu, Xiuping¹ xpliu@dlut.edu.cn
Source:	Computers & Graphics. Aug2018, Vol. 74, p119-125. 7p.
Subjects:	Computers in geometry, Geodesic equation, Kernel functions, Perturbation theory, Gaussian function
Abstract:	Weighted average is one of the most common strategies used in various mesh filters, and its performance depends on the weight design. When computing the weight between the current face and one of its neighbours, existing methods consider only properties of the two faces, such as positions and normals. Although they generate some convincing results, they definitely tend to suffer from cross-region mixing. For example, assigning such a large weight between two nearby faces separated by some feature edges, even when their properties are close, will damage the local structure. In this paper, we present a novel mesh filter model, named as Propagated Mesh Normal Filtering. It estimates the weight between the current face and its neighbours based on the integral of two kinds of face normal differences along the geodesic path, connecting them. Therefore, prominent features are better preserved when removing noises or textures. Furthermore, in view of the sparseness of large normal difference for most of geometry shapes, the L 1 norm is employed when integrating to further improve the filter. Experiments illustrate the enhanced efficacy of our propagated filter comparing with state-of-the-art methods. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Weighted average is one of the most common strategies used in various mesh filters, and its performance depends on the weight design. When computing the weight between the current face and one of its neighbours, existing methods consider only properties of the two faces, such as positions and normals. Although they generate some convincing results, they definitely tend to suffer from cross-region mixing. For example, assigning such a large weight between two nearby faces separated by some feature edges, even when their properties are close, will damage the local structure. In this paper, we present a novel mesh filter model, named as Propagated Mesh Normal Filtering. It estimates the weight between the current face and its neighbours based on the integral of two kinds of face normal differences along the geodesic path, connecting them. Therefore, prominent features are better preserved when removing noises or textures. Furthermore, in view of the sparseness of large normal difference for most of geometry shapes, the L 1 norm is employed when integrating to further improve the filter. Experiments illustrate the enhanced efficacy of our propagated filter comparing with state-of-the-art methods. [ABSTRACT FROM AUTHOR]
ISSN:	00978493
DOI:	10.1016/j.cag.2018.05.003