Method for denoising triangular mesh based on dual graph neural network

Abstract

The invention discloses a method, device and equipment for denoising a triangular mesh based on a dual graph neural network and a storage medium thereof, and the method comprises the steps: constructing a graph data structure for vertexes and triangular patches in the triangular mesh at the same time to form a dual graph, and inputting a vertex graph and a patch graph into a corresponding graph neural network; the graph structure established based on the vertexes is used for pre-denoising noise vertexes in the neural network, and the graph structure established based on the patches is used for denoising the normal direction of the patches in the neural network; and updating the pre-denoised vertexes according to the normal direction of the denoised patch so as to output a final triangular mesh. According to the scheme, the topological structure of the triangular mesh is better utilized, the denoising effect is better, geometric details are better kept, and in quantization errors, the average error of the normal angle of the patch and the average error of the vertex distance are smaller.

Description

technical field [0001] The invention relates to computer software, in particular to a method, device, equipment and storage medium for denoising triangular grids based on a dual graph neural network. Background technique [0002] In the fields of 3D reconstruction, augmented reality, medicine, etc., it is an essential step to scan and reconstruct the mesh model of an object or human body. However, due to the accuracy of scanning sensors and other equipment, ambient light and other reasons, the reconstructed grid will inevitably contain noise, which will greatly affect the subsequent visualization effects. [0003] Triangular mesh is one of the most common representation forms of 3D geometry, which includes a set of 3D point sets and a set of facets, each triangular facet consists of three point indices. Due to the noise triangular mesh generated by various reasons, the three-dimensional vertices are shifted, resulting in the inability to accurately represent the local geome...

AI Technical Summary

Problems solved by technology

However, these methods ignore two points: (1) Since the vertices of the noisy grid itself contain noise, it is difficult to perform normal regression directly from these noisy att...

Images