Efficient mesh denoising via robust normal filtering and alternate vertex updating

Tao LI; Jun WANG; Hao LIU; Li-gang LIU

doi:10.1631/FITEE.1601229

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Efficient mesh denoising via robust normal filtering and alternate vertex updating

Regular Papers | Updated：2022-05-19

- Efficient mesh denoising via robust normal filtering and alternate vertex updating
- 基于鲁棒法矢滤波和交替顶点更新的有效网格去噪
- Frontiers of Information Technology & Electronic Engineering Vol. 18, Issue 11, Pages: 1828-1842(2017)
- Affiliations：
  
  College of Mathematics and Physics, Suzhou University of Science and Technology, Suzhou 215007, China
  College of Mechanical and Electronics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  College of Mathematics Sciences, University of Science and Technology of China, Hefei 230026, China
- Author bio：
  
  Tao LI, E-mail: litao@mail.usts.edu.cn
  [ "Jun WANG, E-mail: wjun@nuaa.edu.cn" ]
  [ "Hao LIU, E-mail: liuhao-01@nuaa.edu.cn" ]
  [ "Li-gang LIU, E-mail: lgliu@ustc.edu.cn" ]
- Funds：
- DOI：10.1631/FITEE.1601229
  CLC： TP391.7
- Received：04 May 2016，
  
  Revised：2017-11-;8，
  
  Published：2017-11
- Accepted：
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Tao LI, Jun WANG, Hao LIU, et al. Efficient mesh denoising via robust normal filtering and alternate vertex updating[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(11): 1828-1842.
DOI：

Tao LI, Jun WANG, Hao LIU, et al. Efficient mesh denoising via robust normal filtering and alternate vertex updating[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(11): 1828-1842. DOI： 10.1631/FITEE.1601229.

摘要

区分特征和噪声是网格去噪中最具挑战性的问题。本文基于鲁棒的引导法矢估计和交替顶点更新策略，研究了一种新的、保持特征的网格去噪方法。为了准确地捕捉特征周围的局部结构，我们提出了一种角点敏感的邻域（corner-aware neighborhood

CAN）方案。本文将CAN中所有面的总体法矢分布及其相应面的个体法矢影响相结合，提出了一种新的一致性度量方法，大大提高了引导法矢估计的可靠性。随着噪声水平的降低，我们用前次迭代的得到滤波法矢作为引导进行联合双边滤波，其思想与新出现的rolling guidance方法是一致的。在顶点更新过程中，我们在每次迭代时都根据滤波后的法线对顶点进行分类，并在各自的正则化约束下交替地对不同类型的顶点进行重新定位。对各种合成数据和实际数据的实验表明，该方法能适应高斯噪声和脉冲噪声等不同类型的噪声，且无论噪声沿法矢方向还是沿随机方向分布，都不会出现翻转的三角片。

Abstract

The most challenging problem in mesh denoising is to distinguish features from noise. Based on the robust guided normal estimation and alternate vertex updating strategy

we investigate a new feature-preserving mesh denoising method. To accurately capture local structures around features

we propose a corner-aware neighborhood (CAN) scheme. By combining both overall normal distribution of all faces in a CAN and individual normal influence of the interested face

we give a new consistency measuring method

which greatly improves the reliability of the estimated guided normals. As the noise level lowers

we take as guidance the previous filtered normals

which coincides with the emerging rolling guidance idea. In the vertex updating process

we classify vertices according to filtered normals at each iteration and reposition vertices of distinct types alternately with individual regularization constraints. Experiments on a variety of synthetic and real data indicate that our method adapts to various noise

both Gaussian and impulsive

no matter in the normal direction or in a random direction

with few triangles flipped.

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