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Related Experiment Videos

Robust feature-preserving mesh denoising based on consistent subneighborhoods.

Hanqi Fan¹, Yizhou Yu, Qunsheng Peng

¹Zhejiang University, Hangzhou.

IEEE Transactions on Visualization and Computer Graphics

|January 16, 2010

Summary

This study presents a novel feature-preserving denoising algorithm for noisy 3D meshes. The method effectively preserves sharp features like edges and corners by identifying consistent surface segments for accurate data smoothing.

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Area of Science:

Computer Graphics
Geometric Modeling
Computational Geometry

Background:

Noisy 3D meshes pose challenges in computer graphics and geometric modeling.
Preserving sharp features during denoising is crucial for accurate representation.
Existing algorithms often struggle to maintain geometric integrity and sharp features.

Purpose of the Study:

Introduce a novel feature-preserving denoising algorithm for 3D meshes.
Develop a method that robustly identifies and preserves sharp geometric features.
Enhance the quality of noisy mesh data while maintaining surface integrity.

Main Methods:

Utilizes a piecewise smooth surface assumption where sharp features are intersections of smooth regions.
Defines a consistent subneighborhood for each vertex to isolate relevant surface segments.
Employs a density-based clustering algorithm for identifying piecewise smooth subneighborhoods.
Applies robust local quadric fitting for initial normal and curvature tensor estimation.
Incorporates anisotropic and second-order bilateral filtering, guided by consistent subneighborhoods, for smoothing.

Main Results:

The algorithm successfully preserves sharp features such as edges and corners in both generic and CAD models.
Demonstrates effective denoising while maintaining the underlying piecewise smooth surface structure.
Alleviates volume shrinkage commonly observed in other denoising techniques.
Preserves curvature details through advanced filtering techniques.

Conclusions:

The proposed feature-preserving denoising algorithm offers a robust solution for enhancing noisy 3D mesh data.
It effectively balances noise reduction with the critical preservation of sharp geometric features.
The method shows significant promise for applications in computer graphics, CAD, and geometric processing.