Constraint-based point set denoising using normal voting tensor and restricted quadratic error metrics

Yadav, Sunil Kumar; Reitebuch, Ulrich; Skrodzki, Martin; Zimmermann, Eric; Polthier, Konrad

doi:10.1016/j.cag.2018.05.014

Cited by 29 publications

(36 citation statements)

References 21 publications

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“…We contrast our method with the nine most closely related mesh denoising methods, which are the bilateral mesh denoising method (BM) [3], the noniterative, featurepreserving mesh smoothing method (NoIter) [27], the Fast method [28], the local scheme of bilateral normal filtering (BNF method) [4], the L0 method [23], the GMNF method [5], the ENVT method [45], the RoFi method [32] and the CPD method [9]. To make our comparison fair, some results are provided by these authors, and the unprovided data are obtained through experiments with the code provided by the authors.…”

Section: Resultsmentioning

confidence: 99%

“…Note that to make a fair comparison, we enumerated the dense sample set in the parameter space of each method and selected the best result from the sample parameters. Some results of the algorithm (such as GMNF [5], ENVT [45], RoFi [32] and CPD [9]) are provided by the author. By analyzing and comparing the results, we can obtain the conclusions below.…”

Section: Qualitative Comparison Of the Mesh Denoising Resultsmentioning

confidence: 99%

“…To solve this problem, the ENVT method [45] calculates the voting tensor, uses the binary optimization quantum element method (QEM) of eigenvalues to classify the feature points, uses the voting tensor feature vector to filter the surface normals anisotropically, and finally updates the vertices to adapt to the filtered face normals. The CPD method [9] is improved on the basis of the ENVT method [45]. Because vertex normals are very sensitive to noise, it is challenging to deal with noisy models.…”

Section: Anisotropic Mesh Filteringmentioning

confidence: 99%

“…Feature points are an important representation of the geometric structure attributes of 3D models; they constitute the contour features of 3D models and highlight the boundary and geometric structure of 3D scenes, which are the key basis for retaining and restoring structural features in the process of 3D model mesh denoising. To better retain sharp features, researchers began to explore feature-based denoising methods [7][8][9], they first classify feature vertices and then apply different denoising strategies to update the vertex position. However, due to the noise sensitivity of surface normals, the detected features may suffer from information loss or confusion.…”

Section: Introductionmentioning

confidence: 99%

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3D Mesh Pre-Processing Method Based on Feature Point Classification and Anisotropic Vertex Denoising Considering Scene Structure Characteristics

et al. 2021

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3D mesh denoising plays an important role in 3D model pre-processing and repair. A fundamental challenge in the mesh denoising process is to accurately extract features from the noise and to preserve and restore the scene structure features of the model. In this paper, we propose a novel feature-preserving mesh denoising method, which was based on robust guidance normal estimation, accurate feature point extraction and an anisotropic vertex denoising strategy. The methodology of the proposed approach is as follows: (1) The dual weight function that takes into account the angle characteristics is used to estimate the guidance normals of the surface, which improved the reliability of the joint bilateral filtering algorithm and avoids losing the corner structures; (2) The filtered facet normal is used to classify the feature points based on the normal voting tensor (NVT) method, which raised the accuracy and integrity of feature classification for the noisy model; (3) The anisotropic vertex update strategy is used in triangular mesh denoising: updating the non-feature points with isotropic neighborhood normals, which effectively suppressed the sharp edges from being smoothed; updating the feature points based on local geometric constraints, which preserved and restored the features while avoided sharp pseudo features. The detailed quantitative and qualitative analyses conducted on synthetic and real data show that our method can remove the noise of various mesh models and retain or restore the edge and corner features of the model without generating pseudo features.

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Section: Resultsmentioning

confidence: 99%

Section: Qualitative Comparison Of the Mesh Denoising Resultsmentioning

confidence: 99%

Section: Anisotropic Mesh Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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3D Mesh Pre-Processing Method Based on Feature Point Classification and Anisotropic Vertex Denoising Considering Scene Structure Characteristics

et al. 2021

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“…However, a post‐processing operation is required to solve the cross artifact problem in the area with sharp edges. Moreover, Yadav et al [YRS*18] used a framework based on a normal voting tensor to denoise anisotropic point sets. They apply restricted quadratic error metrics on the vertex normals, which are processed by a vertex‐based normal voting tensor and binary eigenvalues optimization.…”

Section: Related Workmentioning

confidence: 99%

Reliable Rolling‐guided Point Normal Filtering for Surface Texture Removal

Sun

Chen

Qin

et al. 2019

Computer Graphics Forum

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Semantic surface decomposition (SSD) facilitates various geometry processing and product re‐design tasks. Filter‐based techniques are meaningful and widely used to achieve the SSD, which however often leads to surface either under‐fitting or over‐fitting. In this paper, we propose a reliable rolling‐guided point normal filtering method to decompose textures from a captured point cloud surface. Our method is built on the geometry assumption that 3D surfaces are comprised of an underlying shape (US) and a variety of bump ups and downs (BUDs) on the US. We have three core contributions. First, by considering the BUDs as surface textures, we present a RANSAC‐based sub‐neighborhood detection scheme to distinguish the US and the textures. Second, to better preserve the US (especially the prominent structures), we introduce a patch shift scheme to estimate the guidance normal for feeding the rolling‐guided filter. Third, we formulate a new position updating scheme to alleviate the common uneven distribution of points. Both visual and numerical experiments demonstrate that our method is comparable to state‐of‐the‐art methods in terms of the robustness of texture removal and the effectiveness of the underlying shape preservation.

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