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 ...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.展开更多
In this paper, we propose anovel geometricaldetail editing method for triangulatedmeshmodels based on filtering robust differential edge coordinates.Theintroduceddetail editing consists ofnot only feature-preserving d...In this paper, we propose anovel geometricaldetail editing method for triangulatedmeshmodels based on filtering robust differential edge coordinates.Theintroduceddetail editing consists ofnot only feature-preserving denoising for removing scanner noises, but also interactive detail editing for weakening or enhancing some specific geometric details.Various 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 sense.Experimental results and comparisonswith other methodsdemonstrate that our method is effective and robust.展开更多
基金supported in part by the National Institutes of Health of USA under Grant No. R15HL103497 from the National Heart, Lung, and Blood Institute (NHLBI)a subcontract of NIH Award under Grant No. P41RR08605 from the National Biomedical Computation Resource
文摘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.
基金Supported by National Natural Science Foundation of China(Nos.61402300,61373160,61363048,61173102,61370143)Natural Science Foundation of Hebei Province(F2014210127)Funded Projects for Introduction of Overseas Scholars of Hebei Province
文摘In this paper, we propose anovel geometricaldetail editing method for triangulatedmeshmodels based on filtering robust differential edge coordinates.Theintroduceddetail editing consists ofnot only feature-preserving denoising for removing scanner noises, but also interactive detail editing for weakening or enhancing some specific geometric details.Various 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 sense.Experimental results and comparisonswith other methodsdemonstrate that our method is effective and robust.
