Triangle Mesh Segmentation and Denoising for Sharp Feature Preservation
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Solution Overview
Problem
Conventional triangular mesh denoising methods face issues such as the destruction of sharp features due to unequal weighting of isotropic and anisotropic points, complex parameter tuning, and high computational demands, leading to reduced data quality and processing speed.
Innovation Solution
A segmenting and denoising method that includes reading triangle mesh data, determining noise levels, segmenting using a region growing algorithm, optimizing with a hole-filling algorithm, and filtering using fast, bilateral, or L1 median filtering algorithms to preserve boundary features and enhance denoising efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If local neighborhood information is used for denoising with isotropic and anisotropic weighting, then denoising is performed, but sharp features are destroyed to some extent
Solution Approach 1:
The patent segments the triangle mesh into multiple sub-regions based on curvature characteristics (high curvature regions for sharp features, low curvature regions for smooth surfaces). This segmentation allows different denoising strategies to be applied to different regions, preserving sharp features while removing noise.
Solution Approach 2:
The patent applies different denoising weights to different regions: high curvature regions (sharp features) receive larger weights to preserve them, while low curvature regions (smooth surfaces) receive smaller weights for more aggressive noise removal. This local quality differentiation resolves the contradiction between noise removal and feature preservation.
2Object-affected harmful factors
If parameters such as angle and distance are introduced to suppress anisotropic points, then the influence of anisotropic points is reduced, but parameter tuning difficulty increases and expected effects are hard to achieve
Solution Approach 1:
The patent changes the parameter representation from geometric parameters (angle, distance) to curvature-based parameters (high curvature, low curvature regions). This transformation simplifies the parameter tuning process while effectively suppressing anisotropic points and preserving sharp features.
3Object-affected harmful factors
If more information is used for mesh denoising, then denoising is performed, but computational amount increases and processing speed decreases
Solution Approach 1:
The patent segments the mesh into high and low curvature regions, allowing selective processing. Only necessary computations are performed in each region, reducing overall computational complexity while maintaining denoising effectiveness.
Solution Approach 2:
The patent applies different computational strategies to different regions: simplified processing for low curvature regions and targeted processing for high curvature regions. This local quality approach reduces unnecessary computations and improves processing speed.
Data Source
AI summary
A method for segmenting and denoising a triangle mesh, the method comprising: reading triangle mesh data containing N triangular patches, determining the noise level of the triangle mesh data, and optimizing data at a noise level higher than a preset value; segmenting the triangle mesh data by using a region growing segmentation algorithm, such that a plurality of sub-regions of the triangle mesh data are formed; optimizing the segmented triangle mesh data by using a hole-filling algorithm; and filtering the segmented triangle mesh data by using a denoising algorithm.