A Deviation-Based Dynamic Vertex Reordering Technique for 2D Mesh Quality Improvement

Choi, Ji Sun; Kim, Harrim; Sastry, Shankar Prasad; Kim, Jibum

doi:10.3390/sym11070895

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…In contrast, local mesh smoothing (Gauss-Seidel type) subdivides the entire mesh into discrete patches for each free vertex and iteratively performs mesh smoothing for each patch until user-defined termination criteria are met [11]. For local mesh smoothing, the order free vertices are moved can affect final output mesh quality and/or efficiency [31]. Figure 2 shows that each patch includes the layer of elements adjacent to a free vertex.…”

Section: A Mesh Smoothingmentioning

confidence: 99%

A Data-Driven Approach for Simultaneous Mesh Untangling and Smoothing Using Pointer Networks

2020

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Poorly-shaped and/or inverted elements negatively affect numerical simulation accuracy and efficiency. Most current approaches consider mesh quality improvement and mesh untangling problems as numerical optimization problems and solve them using nonlinear optimization. However, these optimization-based approaches require users to set and solve complex numerical optimization problems with no guarantee that the output meshes are valid meshes with good element qualities. Therefore, this paper proposes a data-driven approach for simultaneous mesh untangling and smoothing using a Pointer network. The proposed approach generates various triangular meshes and employs a novel sequence generation algorithm to train the Pointer network and predicts competitive approximate solutions using the trained network. The strength of the proposed framework lies in its simplicity to predict the best free vertex candidate, providing high-quality output meshes, since it does not require solving complex numerical optimization for prediction. Experimental results show that the proposed framework successfully eliminates inverted elements on the meshes and improved average and worst element quality up to 85.9% and 97.8%, respectively, compared to current optimization-based methods. INDEX TERMS Mesh smoothing, mesh untangling, pointer network, deep learning, recurrent neural network.

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Section: A Mesh Smoothingmentioning

confidence: 99%

A Data-Driven Approach for Simultaneous Mesh Untangling and Smoothing Using Pointer Networks

2020

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“…[15][16][17]. Geometric primitives could be reordered [3,18], structured [19], or processed in the GPU [3,20] to improve efficiency of visibility culling.…”

Section: Introductionmentioning

confidence: 99%

Mesh Clustering and Reordering Based on Normal Locality for Efficient Rendering

Kim

Lee

2022

Symmetry

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Recently, the size of models for real-time rendering has been significantly increasing for realism, and many graphics applications are being developed in mobile devices with relatively insufficient hardware power. Therefore, improving rendering speed is still important in graphics. Back-face culling is one of the core speed-up techniques to remove the back-facing polygons that are not drawn in the result image. In this paper, we present a mesh clustering and reordering method based on normal coherence for efficient back-face culling at an earlier stage than the current method, which removes back faces after the vertex shader on the GPU. In the pre-computation, our method first vertically clusters the mesh into multiple stripes based on the latitude of the face normal vector and sorts each stripe in ascending order of longitude. At runtime, our method computes a potentially visible set of faces at the current camera view by excluding back faces from the clustered and reordered faces list, and draws only the potentially visible set. Experiments have shown that the rendering using our method is more efficient than traditional methods, especially for large and static models.

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“…Mesh clean-up and mesh smoothing are the two major categories of optimization. The former improves the mesh quality by changing the original topology [2,3], such as by encryption and reordering [4]. Mesh smoothing, which relocates vertices to improve the mesh quality, is a simpler method and retains the mesh topology [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Efficient Parallel Algorithms for 3D Laplacian Smoothing on the GPU

Xiao

Yang

Zhao³

et al. 2019

Applied Sciences

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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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A Deviation-Based Dynamic Vertex Reordering Technique for 2D Mesh Quality Improvement

Cited by 4 publications

References 12 publications

A Data-Driven Approach for Simultaneous Mesh Untangling and Smoothing Using Pointer Networks

A Data-Driven Approach for Simultaneous Mesh Untangling and Smoothing Using Pointer Networks

Mesh Clustering and Reordering Based on Normal Locality for Efficient Rendering

Efficient Parallel Algorithms for 3D Laplacian Smoothing on the GPU

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