A cascaded approach for feature-preserving surface mesh denoising

Wang, Jun; Zhang, Xi; Yu, Zeyun

doi:10.1016/j.cad.2012.03.001

Cited by 60 publications

(34 citation statements)

References 20 publications

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“…We [11], and He and Schaefer's area-based edge filter [2]. All these approaches are anisotropic filters which, in general, can better preserve features than isotropic ones.…”

Section: Resultsmentioning

confidence: 99%

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Bi-Normal Filtering for Mesh Denoising

Wei

Pang

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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Most mesh denoising techniques utilize only either the facet normal field or the vertex normal field of a mesh surface. The two normal fields, though contain some redundant geometry information of the same model, can provide additional information that the other field lacks. Thus, considering only one normal field is likely to overlook some geometric features. In this paper, we take advantage of the piecewise consistent property of the two normal fields and propose an effective framework in which they are filtered and integrated using a novel method to guide the denoising process. Our key observation is that, decomposing the inconsistent field at challenging regions into multiple piecewise consistent fields makes the two fields complementary to each other and produces better results. Our approach consists of three steps: vertex classification, bi-normal filtering, and vertex position update. The classification step allows us to filter the two fields on a piecewise smooth surface rather than a surface that is smooth everywhere. Based on the piecewise consistence of the two normal fields, we filtered them using a piecewise smooth region clustering strategy. To benefit from the bi-normal filtering, we design a quadratic optimization algorithm for vertex position update. Experimental results on synthetic and real data show that our algorithm achieves higher quality results than current approaches on surfaces with multifarious geometric features and irregular surface sampling.

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“…We [11], and He and Schaefer's area-based edge filter [2]. All these approaches are anisotropic filters which, in general, can better preserve features than isotropic ones.…”

Section: Resultsmentioning

confidence: 99%

“…More recently, several researchers, e.g., Fan et al [10], Bian and Tong [34], and Wang et al [11], classified vertices on the noisy mesh into different types before denoising process. Wang et al [35] simultaneously decoupled noises and features on 3D surfaces for denoising.…”

Section: Related Workmentioning

confidence: 99%

Bi-Normal Filtering for Mesh Denoising

Wei

Pang

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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“…Wei et al [11], Fan et al [32], Wang et al [33], and Zhu et al [34] propose a multistage framework based on feature detection and normal filtering, which can better preserve features at various scales than the two-stage methods. However, these efforts are still based on the traditional two-stage framework, which cannot completely overcome the disadvantages mentioned earlier.…”

Section: Multistage Methodsmentioning

confidence: 99%

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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“…Wang et al (2012) propõem um método que combina um filtro bilateral com um limiar calculado adaptativamente:…”

Section: Atualização Das Normaisunclassified

“…Este esquema serve como base do mecanismo proposto por Zheng et al (2011), mostrado a seguir: Wang et al (2012) propuseram um método que combina ambos os esquemas explicados anteriormente, usando um peso bilateral consistindo de um peso Gaussiano e de um limiar.…”

Section: Peso Bilateral-gaussianounclassified

Correção de normais para suavização de nuvens de pontos

Valdivia¹

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A cascaded approach for feature-preserving surface mesh denoising

Cited by 60 publications

References 20 publications

Bi-Normal Filtering for Mesh Denoising

Bi-Normal Filtering for Mesh Denoising

Tensor Voting Guided Mesh Denoising

Correção de normais para suavização de nuvens de pontos

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