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Feature-Preserving Mesh Denoising via Anisotropic Surface Fitting

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Abstract

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.

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Authors and Affiliations

  1. Department of Computer Science, University of Wisconsin, Milwaukee, WI, 53211, U.S.A.

    Jun Wang (Member, ACM, IEEE) & Zeyun Yu (Member, ACM, IEEE)

Authors
  1. Jun Wang
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  2. Zeyun Yu
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Correspondence to Zeyun Yu.

Additional information

The work described was supported in part by the National Institutes of Health of USA under Grant No. R15HL103497 from the National Heart, Lung, and Blood Institute (NHLBI) and by a subcontract of NIH Award under Grant No. P41RR08605 from the National Biomedical Computation Resource. The content is solely the responsibility of the authors and does not necessarily represent the official views of the sponsors.

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Wang, J., Yu, Z. Feature-Preserving Mesh Denoising via Anisotropic Surface Fitting. J. Comput. Sci. Technol. 27, 163–173 (2012). https://doi.org/10.1007/s11390-012-1214-3

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  • DOI: https://doi.org/10.1007/s11390-012-1214-3

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