3D-Mesh denoising using an improved vertex based anisotropic diffusion

TL;DR

This paper introduces an improved vertex-based anisotropic diffusion technique for 3D mesh denoising, enhancing filtering performance by employing specific probability density functions and comparing favorably against existing methods.

Contribution

The paper proposes a novel vertex-based nonlinear diffusion method using Laplace, Gaussian, and Rayleigh PDFs, improving mesh denoising effectiveness over traditional approaches.

Findings

The proposed method outperforms Laplacian flow, mean, median, min, and adaptive MMSE filtering.

Experimental results show significant noise reduction and detail preservation.

Evaluation metrics confirm the method's superior performance in vertex and normal-based error measures.

Abstract

This paper deals with an improvement of vertex based nonlinear diffusion for mesh denoising. This method directly filters the position of the vertices using Laplace, reduced centered Gaussian and Rayleigh probability density functions as diffusivities. The use of these PDFs improves the performance of a vertex-based diffusion method which are adapted to the underlying mesh structure. We also compare the proposed method to other mesh denoising methods such as Laplacian flow, mean, median, min and the adaptive MMSE filtering. To evaluate these methods of filtering, we use two error metrics. The first is based on the vertices and the second is based on the normals. Experimental results demonstrate the effectiveness of our proposed method in comparison with the existing methods.