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Related Experiment Videos

Bilateral normal filtering for mesh denoising.

Youyi Zheng1, Hongbo Fu, Oscar Kin-Chung Au

  • 1Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kownloon, Hong Kong. youyi@cse.ust.hk

IEEE Transactions on Visualization and Computer Graphics
|December 22, 2010
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Mesh Analysis01:20

Mesh Analysis

Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

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This study introduces a new mesh denoising method that preserves features by denoising normal fields. It offers both fast, iterative, and robust global denoising schemes for improved results on noisy or irregularly sampled meshes.

Area of Science:

  • Computer Graphics
  • Geometric Modeling
  • Image Processing

Background:

  • Feature-preserving mesh denoising is essential for maintaining geometric integrity.
  • Existing methods often struggle with preserving sharp features and handling irregular sampling.
  • Current normal filtering techniques typically operate on the Gauss sphere, limiting their direct application to mesh geometry.

Purpose of the Study:

  • To develop a novel anisotropic mesh denoising framework based on normal field denoising.
  • To introduce a bilateral normal filter that considers both spatial and signal distances.
  • To present both local iterative and global non-iterative denoising schemes for enhanced performance and robustness.

Main Methods:

  • Treating mesh normals as a surface signal defined over the original mesh.

Related Experiment Videos

  • Designing a novel bilateral normal filter incorporating spatial and signal distance dependencies.
  • Implementing a local, iterative denoising scheme for high noise levels and a global, non-iterative scheme for irregular sampling.

    • Main Results:

    • The proposed bilateral normal filter is a more natural extension of image denoising bilateral filters.
    • The local scheme demonstrates faster and more effective denoising for extremely noisy meshes.
    • The global scheme shows increased robustness against irregular surface sampling.
    • Both schemes yield visually and numerically superior denoising results compared to previous methods, particularly in challenging regions.

    Conclusions:

    • The presented framework effectively decouples local geometric features from spatial location for feature-preserving mesh denoising.
    • The novel bilateral normal filter and dual denoising schemes offer significant improvements over existing techniques.
    • The method achieves state-of-the-art performance in preserving sharp features and handling irregular mesh sampling.