Three-dimensional Mesh model denoising method capable of reserving features

Abstract

The invention relates to a three-dimensional Mesh model denoising method capable of reserving features. The method comprises steps of firstly, calculating guide normal vectors of all triangular surfaces, and using the guide normal vectors for filtering the normal vectors of all the triangular surfaces based on a joint bilateral filtering algorithm; secondly, classifying feature points by using thefiltered triangular surface normal vector based on a normal voting tensor method, enhancing weak features, and removing pseudo features; then, updating non-feature vertexes based on the normal vectorconstraint terms after neighbor triangular surface filtering, and denoising a non-feature region to obtain optimized normal vectors of non-feature points; clustering a support neighborhood point setof the feature points according to the similarity of the feature vectors of the tensor matrix and the vertex normal vectors, and fitting a support plane of the feature points; and finally, updating the feature points based on the normal vector constraint term of the neighbor triangular surface and the constraint term of the support plane, and denoising the feature region. According to the method,the problems of feature oversmoothness and feature loss in the denoising process of the three-dimensional Mesh model can be solved, so that the three-dimensional Mesh model with features reserved after noise removal is obtained.

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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