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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  > In Press, Accepted Manuscript
J. Yang, C. Wang, H. Hou, M. Wang, and X. Li, “Feature-driven variational mesh denoising,” IEEE/CAA J. Autom. Sinica, 2024. doi: 10.1109/JAS.2024.124923
Citation: J. Yang, C. Wang, H. Hou, M. Wang, and X. Li, “Feature-driven variational mesh denoising,” IEEE/CAA J. Autom. Sinica, 2024. doi: 10.1109/JAS.2024.124923
shu

Feature-Driven Variational Mesh Denoising

doi: 10.1109/JAS.2024.124923
Funds:  This work was supported in part by the National Natural Science Foundation of China (62476219, 62206220, 12271140, 12326609), the Young Talent Fund of Association for Science and Technology in Shaanxi, China (20230140), the Chunhui Program of Ministry of Education of China (HZKY20220537), and the Fundamental Funds for the Central Universities (G2023KY0601)
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    Abstract
  • This work elaborates an innovative mesh denoising approach that combines feature recovery and denoising in an alternating manner. It proposes a feature-driven variational model and introduces an iterative scheme that alternates between feature recovery and the denoising process. The main idea is to estimate feature candidates, filter noisy face normals in the smooth (non-feature) domain, and utilize erosion and dilation operators on the feature candidates. By imposing connectivity constraints on normal vectors with large amplitude variations, the proposed scheme effectively removes noise and progressively recovers both sharp and small-scale features during the iterative process. To validate its effectiveness, this work conducts extensive numerical experiments on both simulated and real-scanned data. The results demonstrate significant improvements in noise reduction and feature preservation compared to existing methods.

     

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