A feature-preserving 3D mesh model denoising method

Abstract

The invention relates to a feature-preserving three-dimensional Mesh model denoising method. First calculate the guide normal vectors of all triangle faces, and use the guide normal vectors to filter the normal vectors of all triangle faces based on the joint bilateral filtering algorithm; secondly, use the filtered triangle face normal vectors to classify feature points based on the normal voting tensor method, and enhance Weak features and false features are eliminated; then, the non-feature vertices are updated based on the normal vector constraint items filtered by the neighbor triangles, the non-feature areas are denoised and the optimized normal vectors of the non-feature points are obtained; then according to the eigenvectors of the tensor matrix and Vertex normal vector similarity clusters the support neighborhood point set of the feature point, and fits the support plane of the feature point; finally, the feature point is updated based on the normal vector constraint item of the neighbor triangle surface and the constraint item of the support plane, and the feature area is removed. noise. This method can solve the problem of feature over-smoothing and feature loss in the denoising process of the 3D Mesh model, so as to obtain a 3D Mesh model that retains features after noise removal.

Description

technical field [0001] The invention belongs to the field of three-dimensional model optimization processing, in particular to a feature-preserving three-dimensional Mesh model denoising method. Background technique [0002] Common 3D model denoising methods are usually divided into isotropic methods and anisotropic methods, among which isotropic methods include the early Laplacian method, Taubin and methods based on average curvature flow, etc. Anisotropic methods include methods based on filtering method vector Bilateral filtering methods, optimization-based methods, regularization-based methods, learning-based methods, and feature recognition and feature classification methods. [0003] The above method has the following problems: [0004] When updating the vertex position, there are problems that the edge features are not realistic enough and the corner features are lost; on the other hand, the above classic feature-based denoising methods can obtain more accurate than ...

AI Technical Summary

Problems solved by technology

Through this method, two problems in the denoising process of the 3D Mesh model can be solved: (1) The method based on bilateral filtering cannot preserve features well when removing the surface noise of the 3D model; (2) The method based on normal voting tensor It is difficult to classify features from noise models, and the main purpose is to obtain accurate and true 3D low-noise or noise-free models, thereby significantly improving the accuracy and integrity of 3D models after denoising, and avoiding problems such as blurring or loss of structural features

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