Mesh-Based Consensus Distributed Particle Filtering for Sensor Networks

Liu, Ye; Coombes, Matthew; Liu, Cunjia

doi:10.1109/tsipn.2023.3278469

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…Mesh surface filtering [1][2][3][4][5][6][7] is a pivotal process in the domain of computer graphics and computational geometry, aimed at enhancing the visual quality and computational efficiency of three-dimensional (3D) mesh models. A 3D mesh is a discrete representation of a surface composed of vertices, edges, and faces.…”

Section: Introductionmentioning

confidence: 99%

Analysing detail preserving capabilities of bilateral, laplacian and taubin mesh filtering methods

BEŞDOK,

ÇİVİCİOĞLU

2023

Mersin Photogrammetry Journal

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Mesh filtering of surfaces is crucial for noise reduction, feature preservation, and mesh simplification in graphics, visualization, and computer vision. In this paper, the detail preservation capacities of 3 frequently used filters, i.e., Bilateral, Laplacian, and Taubin mesh filters, in mesh filtering have been thoroughly examined by experiments conducted on 4 different test meshes. Bilateral filtering maintains sharp features by combining geometric closeness and intensity resemblance but may be computationally intensive. Laplacian filtering smoothens the surface by averaging neighboring vertices but might over-smooth sharp details, while Taubin filtering iteratively applies Laplacian and high-pass filters, achieving smoothness without shrinking the mesh. The statistical analysis of the experimental results has shown that the Taubin method is statistically a more successful mesh filtering method for the test sets used in this paper.

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