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Segmentation-driven feature-preserving mesh denoising

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Abstract

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.

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The test mesh data set being presented in this paper is available on reasonable request.

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Authors and Affiliations

  1. Deakin University, Geelong, VIC, Australia

    Weijia Wang, Richard Dazeley, Lei Wei & Bernard Rolfe

  2. OPT Machine Vision Tech Co., Ltd. (Japan), Tokyo, Japan

    Wei Pan

  3. OPT Machine Vision Tech Co., Ltd., Dongguan, China

    Chaofan Dai

  4. La Trobe University, Bundoora, VIC, Australia

    Xuequan Lu

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  1. Weijia Wang
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Wang, W., Pan, W., Dai, C. et al. Segmentation-driven feature-preserving mesh denoising. Vis Comput (2023). https://doi.org/10.1007/s00371-023-03161-w

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