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feature-preserving Mesh Denoising via Anisotropic Surface Fitting

Journal of Computer Science & Technology 2012年第1期27卷 163-173页

作者：汪俊余泽云 Department of Computer Science University of Wisconsin Milwaukee WI 53211 U.S.A.

We propose in this paper a robust surface mesh denoising method that can effectively remove mesh noise while faithfully preserving sharp features. This method utilizes surface fitting and projection techniques. Sharp features are preserved in the surface fitting algorithm by considering an anisotropic neighborhood of each vertex detected by the normal-weighted distance. In addition, to handle the mesh with a high level of noise, we perform a pre-filtering of surface normals prior to the neighborhood searching. A number of experimental results and comparisons demonstrate the excellent performance of our method in preserving important surface geometries while filtering mesh noise.

关键词： mesh denoising feature-preserving surface fitting anisotropic filtering

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Robust Mesh Smoothing

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Journal of Computer Science & Technology 2004年第4期19卷 521-528页

作者： Guo-FeiHu Qun-ShengPeng A.R.Forrest StateKeyLaboratoryofCADandCG ZhejiangUniversityHangzhou310027P.R.China SchoolofComputingScieaces.UniversityofEastAnglia NorwichNR47TJU.K.

This paper proposes a vertex-estimation-based, feature-preserving smoothingtechnique for meshes. A robust mesh smoothing operator called mean value coordinates flow isintroduced to modify mean curvature flow and make it more stable. Also the paper proposes athree-pass vertex estimation based on bilateral filtering of local neighbors which is transferredfrom image processing settings and a Quasi-Laplacian operation, derived from the standard Laplacianoperator, is performed to increase the smoothness order of the mesh rapidly whilst denoising meshesefficiently, preventing volume shrinkage as well as preserving sharp features of the mesh. Comparedwith previous algorithms, the result shows it is simple, efficient and robust.

关键词： mesh smoothing mean value coordinates flow robust vertex estimation feature-preserving

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Watertight surface reconstruction method for CAD models based on optimal transport

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Computational Visual Media 2024年第5期10卷 859-872页

作者： Yuanyan Ye Yubo Wang Juan Cao Zhonggui Chen School of Informatics Xiamen UniversityXiamen 361005China School of Mathematical Sciences Xiamen UniversityXiamen 361005China

feature-preserving mesh reconstruction from point clouds is *** methods tend to fit smooth surfaces and cannot be used to reconstruct sharp *** reconstruction methods are sensitive to noise and only interpolate sharp features when points are distributed on feature *** propose a watertight surface reconstruction method based on optimal transport that can accurately reconstruct sharp features often present in CAD *** formalize the surface reconstruction problem by minimizing the optimal transport cost between the point cloud and the reconstructed *** algorithm consists of initialization and refinement *** the initialization step,the convex hull of the point cloud is deformed under the guidance of a transport plan to obtain an initial approximate ***,the mesh surface was optimized using operations including vertex relocation and edge collapses/fips to obtain feature-preserving *** demonstrate that our method can preserve sharp features while being robust to noise and missing data.

关键词： surface reconstruction feature-preserving discrete optimal transportation

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Least-squares images for edge-preserving smoothing

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Computational Visual Media 2015年第1期1卷 27-35页

作者： Hui Wang Junjie Cao Xiuping Liu Jianmin Wang Tongrang Fan Jianping Hu School of Information Science and Technology Shijiazhuang Tiedao University School of Mathematical Sciences Dalian University of Technology School of Sciences Northeast Dianli University

In this paper, we propose least-squares images(LS-images) as a basis for a novel edgepreserving image smoothing method. The LS-image requires the value of each pixel to be a convex linear combination of its neighbors, i.e., to have zero Laplacian, and to approximate the original image in a least-squares sense. The edge-preserving property inherits from the edge-aware weights for constructing the linear combination. Experimental results demonstrate that the proposed method achieves high quality results compared to previous state-of-theart works. We also show diverse applications of LSimages, such as detail manipulation, edge enhancement,and clip-art JPEG artifact removal.

关键词： feature-preserving image enhancement image smoothing least-squares images(LS-images)

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Mesh Detail Editing by Filtering Differential Edge Coordinates

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Computer Aided Drafting,Design and Manufacturing 2014年第4期24卷 1-6页

作者： WANG Hui CAO Jun-jie LIU Xiu-ping FAN Tong-rang WANG Jian-min School of Information Science and Technology Shijiazhuang Tiedao University School of Mathematical Sciences Dalian University of Technology

In this paper, we propose anovel geometricaldetail editing method for triangulatedmeshmodels based on filtering robust differential edge *** editing consists ofnot only feature-preserving denoising for removing scanner noises, but also interactive detail editing for weakening or enhancing some specific geometric *** detail editing results are obtainedby reconstructingthe mesh fromnew processed differential edge coordinates, which are filtered from the view of signal processing, in linear least square *** results and comparisonswith other methodsdemonstrate that our method is effective and robust.

关键词： mesh details editing feature-preserving mesh denoising mesh enhancing

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