Mesh Denoising using Extended ROF Model with<i>L</i><sub>1</sub>Fidelity

Wu, Xiaoqun; Zheng, Jianmin; Cai, Yiyu; Fu, Chi‐Wing

doi:10.1111/cgf.12743

Cited by 37 publications

(29 citation statements)

References 24 publications

(71 reference statements)

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“…The three regions of irregular surface sampling are highlighted. The denoised results of Wu et al [3] with parameters (50,5), Zhang et al [4] (0.2, 80, 10, 1), and ours.…”

Section: Introductionsupporting

confidence: 50%

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Tensor Voting Guided Mesh Denoising

Wei

Liang

Pang

et al. 2017

IEEE Trans. Automat. Sci. Eng.

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Mesh denoising is imperative for improving imperfect surfaces acquired by scanning devices. The main challenge is to faithfully retain geometric features and avoid introducing additional artifacts when removing noise. Unlike the existing mesh denoising techniques that focus only on either the first-order features or high-order differential properties, our approach exploits the synergy when facet normals and quadric surfaces are integrated to recover a piecewise smooth surface. In specific, we vote on surface normal tensors from robust statistics to guide the creation of consistent subneighborhoods subsequently used by moving least squares (MLS). This voting naturally leads to a conceptually simple way that gives a unified mesh-denoising framework for not only handling noise but also enabling the recovering of surfaces with both sharp and small-scale features. The effectiveness of our framework stems from: 1) the multiscale tensor voting that avoids the influence from noise; 2) the effective energy minimization strategy to searching the consistent subneighborhoods; and 3) the piecewise MLS that fully prevents the side effects from different subneighborhoods during surface fitting. Our framework is direct, practical, and easy to understand. Comparisons with the state-of-the-art methods demonstrate its outstanding performance on feature preservation and artifact suppression.Note to Practitioners-Three-dimensional sensing and scanning devices are widely used to capture digital surfaces of real objects and scenes in many scenarios. However, due to occlusion, motion, multiple reflections, and so on, the captured data often suffer from severe contamination with noise, significantly hindering its practical applications. Therefore, it is indispensable to remove noise prior to further processing, which is commonly referred to as mesh denoising. Mesh denoising is a long-standing problem, and remains open in academic as well as industrial applications due to its challenging nature. The state-of-the-art methods either fail to retain most of the original features presented well in the object, or cannot avoid additional artifacts, such as vertex drifts. In contrast, we design a denoising framework aiming at improving the quality of the raw surface by producing a mesh with better perceptual features. The technique developed here can produce high-quality surface data of real objects and scenes, which would facilitate the modeling, reconstruction, and recognition applications in computer-aided design, reverse engineering, 3-D printing, and computer-aided manufacturing.Index Terms-Consistent subneighborhood, feature preserving, mesh denoising, multiscale tensor voting, piecewise moving least squares (pMLS).

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“…The three regions of irregular surface sampling are highlighted. The denoised results of Wu et al [3] with parameters (50,5), Zhang et al [4] (0.2, 80, 10, 1), and ours.…”

Section: Introductionsupporting

confidence: 50%

“…For example, He and Schaefer [41] employ L 0 -norm to minimize the curvature of a surface except at sharp features, and Wu et al [3] and Wang et al [42] perform L 1 optimization to recover sharp features. Zhang et al [4] combine total variation and piecewise constant function space for variational mesh denoising.…”

Section: E Sparse Optimizationmentioning

confidence: 99%

Tensor Voting Guided Mesh Denoising

Wei

Liang

Pang

et al. 2017

IEEE Trans. Automat. Sci. Eng.

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“…This includes marching cubes, dual marching cubes and the many variants designed to reconstruct meshes from implicit surfaces [12,14,13]. Standard mesh denoising methods could also be considered for removing staircasing effects of digital surfaces [8,[15][16][17]. However, they tend either to consider all steps as features or to smooth everything out.…”

Section: Introductionmentioning

confidence: 99%

Digital Surface Regularization by Normal Vector Field Alignment

Cœurjolly¹,

Gueth²,

Lachaud³

2017

Discrete Geometry for Computer Imagery

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Digital objects and digital surfaces are isothetic structures per se. Such surfaces are thus not adapted to direct visualization with isothetic quads, or to many geometry processing methods. We propose a new regularization technique to construct a piecewise smooth quadrangulated surface from a digital surface. More formally we propose a variational formulation which efficiently regularizes digital surface vertices while complying with a prescribed, eventually anisotropic, input normal vector field estimated on the digital structure. Beside visualization purposes, such regularized surface can then be used in any geometry processing tasks which operates on triangular or quadrangular meshes (e.g. compression, texturing, anisotropic smoothing, feature extraction).

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“…In addition, sparse optimization and surface reconstruction for mesh de-noising have increasingly attracted more attention of researchers in recent years. Several novel l0 and l1 sparse optimization method [17,[23][24][25][26] have been proposed to eliminate robustly and reliably noises while preserving features. However, it is crucial to estimate properly the differential geometric properties of mesh in these methods.…”

Section: Introductionmentioning

confidence: 99%

Bi-Normal Mesh Smoothing Based on Vertex Feature

Wang

Duan

2020

IEEE Access

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Aiming at the issue that mesh smoothing is hard to balance in terms of noise removing and feature preserving, in this paper, we combine the facet normal representing global geometric features of the mesh with the vertex normal characterizing local details of the mesh and propose a bi-normal mesh smoothing method based on vertex feature selection (BNBVF). Firstly, the guided filtering algorithm is extended to calculate accurately the facet normal in the field of geometric processing. The key of this portion is computing the guided normal of facet by using a geometric neighboring patch with the most consistent normal. Then, an adaptive tensor voting method is employed to divide the vertices of the mesh into feature vertices and non-feature vertices. Thirdly, a method of the neighborhood facets clustering combining with the plane fitting is proposed to calculate the normal of feature vertex, and the weighting average of first-order neighborhood facets of the vertex is applied to compute the normal of non-feature vertex. Finally, the vertices of the mesh are updated iteratively by combining the geometric information of the facet normal and vertex normal to achieve mesh smoothing. Experimental results demonstrate that the superior performance of our proposed algorithm to state-of-the-art approaches in feature preserving and error reducing. INDEX TERMS mesh smoothing, feature-preserving, bi-normal, guided filtering, tensor voting

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Mesh Denoising using Extended ROF Model withL₁Fidelity

Cited by 37 publications

References 24 publications

Tensor Voting Guided Mesh Denoising

Tensor Voting Guided Mesh Denoising

Digital Surface Regularization by Normal Vector Field Alignment

Bi-Normal Mesh Smoothing Based on Vertex Feature

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Mesh Denoising using Extended ROF Model withL1Fidelity

Cited by 37 publications

References 24 publications

Tensor Voting Guided Mesh Denoising

Tensor Voting Guided Mesh Denoising

Digital Surface Regularization by Normal Vector Field Alignment

Bi-Normal Mesh Smoothing Based on Vertex Feature

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Mesh Denoising using Extended ROF Model withL₁Fidelity