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Related Experiment Video

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AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells
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Mesh Denoising via Adaptive Consistent Neighborhood.

Mingqiang Guo1,2, Zhenzhen Song1,3, Chengde Han1

  • 1School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China.

Sensors (Basel, Switzerland)
|January 13, 2021
PubMed
Summary

This study introduces a new mesh denoising method using guided normal filtering and adaptive neighborhoods. The technique effectively removes noise while preserving crucial geometric features on complex surfaces.

Keywords:
bilateral filteringfeature preservinggraph-cutguided normal filteringmesh denoising

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Area of Science:

  • Computer Graphics
  • Geometric Modeling
  • Image Processing

Background:

  • Mesh denoising is crucial for 3D data processing.
  • Existing methods struggle with preserving fine geometric details and handling complex shapes.
  • Guided normal filtering offers potential but requires accurate neighborhood information.

Purpose of the Study:

  • To develop an advanced mesh denoising technique.
  • To improve the accuracy of guided normal filtering by creating adaptive neighborhoods.
  • To enhance feature preservation and robustness for complex mesh structures.

Main Methods:

  • A two-stage scheme for constructing adaptive consistent neighborhoods.
  • A novel consistency measurement for initial neighborhood selection (patch-shift manner).
  • An iterative graph-cut based scheme to refine neighborhoods, removing geometric features.
  • Guided normal filtering utilizing these adaptive neighborhoods.
  • Vertex updating for final mesh refinement.

Main Results:

  • The proposed method effectively removes noise from 3D meshes.
  • Geometric features are well-preserved, even on complex surfaces.
  • The adaptive consistent neighborhoods lead to a more accurate guide normal field.
  • Quantitative and visual experiments demonstrate superior performance compared to existing methods.

Conclusions:

  • The novel guided normal filtering with adaptive neighborhoods significantly advances mesh denoising.
  • The method is robust and excels at preserving geometric details.
  • This approach offers a superior solution for processing noisy 3D mesh data.