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 a...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.展开更多
基金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)+1 种基金the Chunhui Program of Ministry of Education of China(HZKY20220537)the Fundamental Funds for the Central Universities(G2023KY0601).
文摘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.
