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High Order Mesh Denoising via ℓp-ℓ1 Minimization
2019
IEEE Access
Preserved Fulltext
INDEX TERMS Augmented lagrangian method, iteratively reweighted 1 minimization, mesh denoising, 3D geometry processing. ...
We testify effectiveness of our mesh denoising method on synthetic meshes and a broad variety of scanning data produced by the laser scanner and Kinect sensors. ...
In our opinion, mesh denoising methods can be rough classified into three categories: filter-based, optimization-based, and data-driven methods. ...
doi:10.1109/access.2019.2939362
fatcat:m4vdn5djdvbujgkddz3irvmfrm
DNF-Net: a Deep Normal Filtering Network for Mesh Denoising
2020
IEEE Transactions on Visualization and Computer Graphics
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2020 pre-print arXiv:2006.15510v1 fatcat:zfjlwfx5cjbjdhfehyn5glme4a
Compared with the recent data-driven works on mesh denoising, DNF-Net does not require manual input to extract features and better utilizes the training data to enhance its denoising performance. ...
To better capture local geometry, our network processes the mesh in terms of local patches extracted from the mesh. ...
Data-driven methods for mesh denoising There have been increasing attention on exploiting datadriven methods to denoise meshes. Wang et al. ...
doi:10.1109/tvcg.2020.3001681
pmid:32746260
fatcat:w3y63aywnnh7jix7iu3kfhcdhe
Fast mesh denoising with data driven normal filtering using deep variational autoencoders
2020
IEEE Transactions on Industrial Informatics
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Training and inference are performed in a sliding patch setup reducing the size of the required training data and execution times. ...
In this work, we propose a fast and robust denoising method for dense 3D scanned industrial models. ...
[16] suggested a data-driven method for mesh denoising that uses training sets of noisy objects. ...
doi:10.1109/tii.2020.3000491
fatcat:g2t5nlhegnexnepifvv46nfiee
GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks
[article]
2021
arXiv
pre-print
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Our code and data are available in https://github.com/Jhonve/GCN-Denoiser. ...
In this paper, we present GCN-Denoiser, a novel feature-preserving mesh denoising method based on graph convolutional networks (GCNs). ...
Data-driven methods have attracted a lot of attention lately and several approaches have been introduced for mesh denoising [Li et al. 2020c; Wang et al. 2016; Zhao et al. 2019b ]. ...
arXiv:2108.05128v1
fatcat:mq4nzoxrzzgs5ikvtmbl3zb53y
Fast Feature-Preserving Approach to Carpal Bone Surface Denoising
2018
Sensors
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Both terms are weighted by a normalized mesh Laplacian, which is defined in terms of a data-adaptive kernel similarity matrix in conjunction with matrix balancing. ...
We present a geometric framework for surface denoising using graph signal processing, which is an emerging field that aims to develop new tools for processing and analyzing graph-structured data. ...
For the future work, we plan to incorporate edge-aware filters into our framework to tackle data-driven geometry processing problems. ...
doi:10.3390/s18072379
pmid:30037109
pmcid:PMC6069221
fatcat:lie73rd4rndztmvw73dkcmdvfu
A PDE patch-based spectral method for progressive mesh compression and mesh denoising
2017
The Visual Computer
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In this paper we, for the first time, extend the use of the PDE method to progressive mesh compression and mesh denoising. ...
Experimental results demonstrate the feasibility of our method in both progressive mesh compression and mesh denoising. ...
All the above are of connectivity-driven algorithms that first encode connectivity data and then use them to encode geometry data. Instead, geometrydriven algorithms proceed in an opposite way. ...
doi:10.1007/s00371-017-1431-4
fatcat:vbv2eigmwnf43banegoydowah4
Spatially Adaptive Regularizer for Mesh Denoising
2020
IEEE Access
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Mesh denoising is a fundamental yet not well-solved problem in computer graphics. ...
INDEX TERMS L p norm, mesh denoising, optimization, regularizer, spatially adaptive. ...
Liu for providing us the denoised results of TVNF [26] and ASONF [29] on the benchmark data. ...
doi:10.1109/access.2020.2987046
fatcat:erv6r2ly6jbg7pd53hijhtenoa
Fast mesh denoising with data driven normal filtering using deep variational autoencoders
2020
Zenodo
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Training and inference are performed in a sliding patch setup reducing the size of the required training data and execution times. ...
In this work, we propose a fast and robust denoising method for dense 3D scanned industrial models. ...
The authors in [16] suggest a data-driven method for mesh denoising that uses training sets of noisy objects. ...
doi:10.5281/zenodo.3865275
fatcat:p7dbz6ya2bc5hfgtywielvuipq
Mesh Total Generalized Variation for Denoising
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2101.02322v1 fatcat:doepccmdxvhexg6dtfertdg4t4
Further, we propose a TGV-based variational model to restore the face normal field for mesh denoising. ...
Extensive results and comparisons on synthetic and real scanning data validate that the proposed method outperforms the state-of-the-art methods visually and numerically. ...
Data-driven methods. More recently, the data-driven methods have received increasing attention. Wang et al. ...
arXiv:2101.02322v2
fatcat:mi5nzytlcnh23pdnahwo2vfdo4
Segmentation Based Mesh Denoising
[article]
2020
arXiv
pre-print
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Feature-preserving mesh denoising has received noticeable attention recently. ...
In this paper, we propose a novel clustering method for mesh denoising, which can avoid the disturbance of anisotropic information and be easily embedded into commonly-used mesh denoising frameworks. ...
Recent advances in deep learning lead to new data-driven methods for mesh segmentation [29] . ...
arXiv:2008.01358v2
fatcat:3co3bibmzrh2nmpkl3uvbbqeay
Modified Bilateral Filter for Feature Enhancement in Mesh Denoising
2022
IEEE Access
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INDEX TERMS Bilateral filter, feature enhancement, mesh denoising, mesh normal filtering. ...
In this study, we design a feature enhancement filter that is combined with a conventional denoising filter to remove the noise while enhancing the features. ...
Recently, various studies based on data-driven methods have also been conducted. A data-driven method was proposed by Diebel et al. [27] . ...
doi:10.1109/access.2022.3176961
fatcat:6i2pcfrgx5e6fo6ykeykawk4yy
Robust Mesh Denoising via Triple Sparsity
2019
Sensors
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Mesh denoising is to recover high quality meshes from noisy inputs scanned from the real world. It is a crucial step in geometry processing, computer vision, computer-aided design, etc. ...
In this paper, we present a novel optimization method for robustly denoising the mesh. ...
On the contrary, without any assumptions about the underlying surface, a data-driven method has been employed for mesh denoising [26] . ...
doi:10.3390/s19051001
fatcat:juhvapg6j5brdlmgbuvshqs3ca
Signal Processing on Static and Dynamic 3D Meshes: Sparse Representations and Applications
2019
IEEE Access
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INDEX TERMS Signal processing on static and dynamic meshes, sparse representation theory & algorithms, 3D geometry acquisition and processing. ...
Moreover, the impact of sparse modeling and optimization tools to several 3D mesh processing tasks, such as completion of missing data, feature preserving noise removal, and rejection of outliers, is illustrated ...
, and imposes lowrank to the recovered geometry, depending on the weighting parameter τ . ...
doi:10.1109/access.2019.2894533
fatcat:pistdxwe3zenrievtcktrco33i
NormalNet: Learning-based Normal Filtering for Mesh Denoising
[article]
2019
arXiv
pre-print
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Mesh denoising is a critical technology in geometry processing that aims to recover high-fidelity 3D mesh models of objects from their noise-corrupted versions. ...
The scheme follows the iterative framework of filtering-based mesh denoising. ...
Hence, mesh denoising has become an active research topic in the area of geometry processing. Mesh denoising is an ill-posed inverse problem. ...
arXiv:1903.04015v2
fatcat:hnxzxubvhnfltlzssa7vjntqnq
Mesh Denoising Based on Recurrent Neural Networks
2022
Symmetry
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Inspired by these works, we propose a data-driven mesh denoising method based on recurrent neural networks, which learns the relationship between the feature descriptors and the ground-truth normals. ...
Mesh denoising is a classical task in mesh processing. ...
We would like to thank Peng-Shuai Wang for providing the data and codes for the mesh denoising. ...
doi:10.3390/sym14061233
fatcat:5enjbbwcrndpvdkwdslvm46way
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