Surface Denoising based on Normal Filtering in a Robust Statistics Framework

• URL: http://arxiv.org/abs/2007.00842v1
• Date: Thu, 2 Jul 2020 02:31:24 GMT
• Title: Surface Denoising based on Normal Filtering in a Robust Statistics Framework
• Authors: Sunil Kumar Yadav and Martin Skrodzki and Eric Zimmermann and Konrad Polthier
• Abstract summary: Noise is inevitable during a surface acquisition process using 3D scanners. Noise-removal process (denoising) can be performed by filtering the surface normals first. In many available denoising algorithms, the robustness of noise-free normals is a key factor.
• Score: 2.294014185517203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: During a surface acquisition process using 3D scanners, noise is inevitable and an important step in geometry processing is to remove these noise components from these surfaces (given as points-set or triangulated mesh). The noise-removal process (denoising) can be performed by filtering the surface normals first and by adjusting the vertex positions according to filtered normals afterwards. Therefore, in many available denoising algorithms, the computation of noise-free normals is a key factor. A variety of filters have been introduced for noise-removal from normals, with different focus points like robustness against outliers or large amplitude of noise. Although these filters are performing well in different aspects, a unified framework is missing to establish the relation between them and to provide a theoretical analysis beyond the performance of each method. In this paper, we introduce such a framework to establish relations between a number of widely-used nonlinear filters for face normals in mesh denoising and vertex normals in point set denoising. We cover robust statistical estimation with M-smoothers and their application to linear and non-linear normal filtering. Although these methods originate in different mathematical theories - which include diffusion-, bilateral-, and directional curvature-based algorithms - we demonstrate that all of them can be cast into a unified framework of robust statistics using robust error norms and their corresponding influence functions. This unification contributes to a better understanding of the individual methods and their relations with each other. Furthermore, the presented framework provides a platform for new techniques to combine the advantages of known filters and to compare them with available methods.

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