Motivated by the conception of Lee et al.(2005)’s mesh saliency and Chen (2005)’s contextual discontinuities, a novel adaptive smoothing approach is proposed for noise removal and feature preservation. Mesh saliency...Motivated by the conception of Lee et al.(2005)’s mesh saliency and Chen (2005)’s contextual discontinuities, a novel adaptive smoothing approach is proposed for noise removal and feature preservation. Mesh saliency is employed as a multiscale measure to detect contextual discontinuity for feature preserving and control of the smoothing speed. The proposed method is similar to the bilateral filter method. Comparative results demonstrate the simplicity and efficiency of the presented method, which makes it an excellent solution for smoothing 3D noisy meshes.展开更多
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
The most challenging problem in mesh denoising is to distinguish features from noise. Based on the robust guided normal estimation and alternate vertex updating strategy, we investigate a new feature-preserving mesh d...The most challenging problem in mesh denoising is to distinguish features from noise. Based on the robust guided normal estimation and alternate vertex updating strategy, we investigate a new feature-preserving mesh denoising method. To accurately capture local structures around features, we propose a corner-aware neighborhood （CAN） scheme. By combining both overall normal distribution of all faces in a CAN and individual normal influence of the interested face, wc give a new consistency measuring method, which greatly improves the reliability of the estimated guided normals. As the noise level lowers, we take as guidance the previous filtered normals, which coincides with the emerging roUing guidance idea. In the vertex updating process, we classify vertices according to filtered normals at each iteration and reposition vertices of distinct types alternately with individual regularization constraints. Experiments on a variety of synthetic and real data indicate that our method adapts to various noise, both Gaussian and impulsive, no matter in the normal direction or in a random direction, with fcw triangles flippcd.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Project supported by the National Science Fund for Creative Re-search Groups (No. 60521002), and the National Natural Science Foundation of China (Nos. 60373070 and 60573147)
文摘Motivated by the conception of Lee et al.(2005)’s mesh saliency and Chen (2005)’s contextual discontinuities, a novel adaptive smoothing approach is proposed for noise removal and feature preservation. Mesh saliency is employed as a multiscale measure to detect contextual discontinuity for feature preserving and control of the smoothing speed. The proposed method is similar to the bilateral filter method. Comparative results demonstrate the simplicity and efficiency of the presented method, which makes it an excellent solution for smoothing 3D noisy meshes.
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Project supported by the National Natural Science Foundation of China （Nos. 61402224 and 61222206）, the Natural Science Foundation of Jiangsu Province, China （No. BK2014833）, and the Natural Science Foundation of Suzhou University of Science and Technology, China （No. XKZ201611）.Acknowledgements The authors would like to appreciate Wang-yu ZHANG for providing executable programs. The models used in this paper are courtesy of the AIM Shape Repos- itory, the Stanford 3D Scanning Repository, and Laser Design.
文摘The most challenging problem in mesh denoising is to distinguish features from noise. Based on the robust guided normal estimation and alternate vertex updating strategy, we investigate a new feature-preserving mesh denoising method. To accurately capture local structures around features, we propose a corner-aware neighborhood （CAN） scheme. By combining both overall normal distribution of all faces in a CAN and individual normal influence of the interested face, wc give a new consistency measuring method, which greatly improves the reliability of the estimated guided normals. As the noise level lowers, we take as guidance the previous filtered normals, which coincides with the emerging roUing guidance idea. In the vertex updating process, we classify vertices according to filtered normals at each iteration and reposition vertices of distinct types alternately with individual regularization constraints. Experiments on a variety of synthetic and real data indicate that our method adapts to various noise, both Gaussian and impulsive, no matter in the normal direction or in a random direction, with fcw triangles flippcd.
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.
