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Segmentation-driven feature-preserving mesh denoising

journal contribution
posted on 2023-12-18, 04:30 authored by W Wang, W Pan, C Dai, Richard DazeleyRichard Dazeley, Lei WeiLei Wei, Bernard RolfeBernard Rolfe, X Lu
Feature-preserving mesh denoising has received noticeable attention in visual media, with the aim of recovering high-fidelity, clean mesh shapes from the ones that are contaminated by noise. Existing denoising methods often design smaller weights for anisotropic surfaces and larger weights for isotropic surfaces in order to preserve sharp features, such as edges or corners, on the mesh shapes. However, they often disregard the fact that such small weights on anisotropic surfaces still pose negative impacts on the denoising outcomes and detail preservation results on the shapes. In this paper, we propose a novel segmentation-driven mesh denoising method which performs region-wise denoising, and thus avoids the disturbance of anisotropic neighbour faces for better feature preservation results. Also, our backbone can be easily embedded into commonly used mesh denoising frameworks. Extensive experiments have demonstrated that our method can enhance the denoising results on a wide range of synthetic and real mesh models, both quantitatively and visually.

History

Journal

Visual Computer

Pagination

1-17

Location

Berlin, Germany

ISSN

0178-2789

eISSN

1432-2315

Language

en

Notes

In press

Publication classification

C1 Refereed article in a scholarly journal

Publisher

Springer Science and Business Media LLC

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