A feature-preserving 3D mesh model denoising method

A 3D and model technology, applied in the field of 3D model optimization processing, can solve problems such as difficult to classify features, unable to preserve features well, and achieve the effect of improving fidelity

Active Publication Date: 2022-05-13
深圳市数字城市工程研究中心 +1
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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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  • A feature-preserving 3D mesh model denoising method
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[0067] The present invention is a feature-preserving three-dimensional Mesh model denoising method based on the joint bilateral filtering algorithm and the normal voting tensor method. The technical process is as follows: figure 1 shown. In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail through specific examples and related drawings.

[0068] 1. Calculate the guide normal vector of the triangle of interest

[0069] First select the bootstrap patch for all triangles of interest. The patch of the triangle of interest is a set of triangles formed by itself and a triangle in a ring of neighboring triangles as the center, such as figure 2 shown. in figure 2 (a) The highlighted triangle is the triangle of interest f i , neighbor triangle f j and all its neighbor triangles constitute a set of patch triangles, triangle f j is the central triangle of the corresponding patc...

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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 ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06T15/00
CPCG06T5/002G06T15/00G06T2207/20028
Inventor 刘亚文邱伟彭哲郭丙轩
Owner 深圳市数字城市工程研究中心
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