Kernel-Based Laplacian Smoothing Method for 3D Mesh Denoising

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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Image-Based 3D MESH Denoising Through A Block Matching 3D Convolutional Neural Network Filtering Approach

2020 IEEE International Conference on Multimedia and Expo (ICME) ◽

10.1109/icme46284.2020.9102938 ◽

2020 ◽

Author(s):

Gerasimos Arvanitis ◽

Aris S. Lalos ◽

Konstantinos Moustakas

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Block Matching ◽

3D Mesh ◽

Mesh Denoising ◽

Filtering Approach

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A new weighted normal-based filter for 3D mesh denoising

2018 9th International Symposium on Signal, Image, Video and Communications (ISIVC) ◽

10.1109/isivc.2018.8709194 ◽

2018 ◽

Author(s):

Mohammed El Hassouni ◽

Aladine Chetouani ◽

Rachid Jennane ◽

Hocine Cherifi

Keyword(s):

3D Mesh ◽

Mesh Denoising

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Mesh Denoising Based on Spherical Wavelets in Reverse Engineering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.232.904 ◽

2012 ◽

Vol 232 ◽

pp. 904-907

Author(s):

Jian Ping Hu ◽

Qi Xie

Keyword(s):

Reverse Engineering ◽

Wavelet Domain ◽

Spherical Wavelets ◽

3D Mesh ◽

Mesh Denoising ◽

Spherical Parameterization ◽

Low Distortion

This paper presents an approach for removing noises of 3D meshes using spherical wavelets. An input 3D mesh is firstly transformed into a spherical signal by a fast low distortion spherical parameterization. This spherical signal is then sampled regularly on the sphere and transformed into the wavelet domain where noises of 3D meshes can be removed easily. Experiments are taken with some typical 3D meshes, which reveals that our method can obtain good results.

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Efficient Parallel Algorithms for 3D Laplacian Smoothing on the GPU

Applied Sciences ◽

10.3390/app9245437 ◽

2019 ◽

Vol 9 (24) ◽

pp. 5437

Author(s):

Lei Xiao ◽

Guoxiang Yang ◽

Kunyang Zhao ◽

Gang Mei

Keyword(s):

Large Scale ◽

Graphics Processing Unit ◽

Parallel Implementation ◽

Three Dimensional ◽

Smoothing Method ◽

Three Dimensions ◽

Processing Unit ◽

Mesh Quality ◽

Mesh Smoothing ◽

Laplacian Smoothing

In numerical modeling, mesh quality is one of the decisive factors that strongly affects the accuracy of calculations and the convergence of iterations. To improve mesh quality, the Laplacian mesh smoothing method, which repositions nodes to the barycenter of adjacent nodes without changing the mesh topology, has been widely used. However, smoothing a large-scale three dimensional mesh is quite computationally expensive, and few studies have focused on accelerating the Laplacian mesh smoothing method by utilizing the graphics processing unit (GPU). This paper presents a GPU-accelerated parallel algorithm for Laplacian smoothing in three dimensions by considering the influence of different data layouts and iteration forms. To evaluate the efficiency of the GPU implementation, the parallel solution is compared with the original serial solution. Experimental results show that our parallel implementation is up to 46 times faster than the serial version.

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Remeshing Based Mesh Smoothing by 2D Sketches Input

Volume 1: 22nd Computers and Information in Engineering Conference ◽

10.1115/detc2002/cie-34478 ◽

2002 ◽

Author(s):

Charlie C. L. Wang ◽

Yu Wang ◽

Matthew M. F. Yuen

Keyword(s):

Thin Plate ◽

Geometric Modeling ◽

Smoothing Method ◽

Mesh Smoothing ◽

Topology Structure ◽

3D Mesh ◽

Mesh Surface ◽

Membrane Energy ◽

Interactive 3D ◽

2D Sketches

This paper presents an interactive 3D mesh smoothing method, which is useful for intuitive, efficient geometric modeling of freeform polygonal models. With our method, users can remove unwanted bumps and cavities, or smooth creases by drawing two strokes to map out the smoothing area. Discrete fairing is applied on the surrounded area to minimize its membrane energy and its thin plate energy. During the process of fairing, an umbrella operator smoothes the vertices in the surrounded area one by one; and a remeshing scheme is used to optimize the topology structure of the mesh inside the surrounded area during smoothing. Compare to other well-known approaches, our method will not be influenced by the topology structure of the smoothing mesh. Thus, it can be applied to any triangular mesh surface. At the end of this paper, examples of our mesh smoothing implementation are shown to demonstrate its functionality.

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