Hybrid geometry / topology based mesh segmentation for reverse engineering

Mejía, Daniel; Ruiz-Salguero, Oscar; Sánchez, Jairo R.; Posada, Jorge; Moreno, Aitor; Cadavid, Carlos A.

doi:10.1016/j.cag.2018.03.004

Cited by 9 publications

(4 citation statements)

References 41 publications

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“…The segmentation accuracy rate was as high as 96%. Mejia et al [24] presented an implementation of a hybrid geometry / topology mesh segmentation algorithm, which yields not only a parameterizable mesh but also a functional partition of scanned mechanical workpieces without resorting to oversegmentation. The algorithm allows automatic processing of 3D meshes from scanned workpieces, improving the reverse engineering workflow.…”

Section: Other Methodsmentioning

confidence: 99%

An Online Rapid Mesh Segmentation Method Based on an Online Sequential Extreme Learning Machine

et al. 2019

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The existing mesh segmentation methods currently require long training times and have high computational complexity. Consequently, many of these methods cannot meet the rapid requirements of digital geometry processing in the Web environment. This paper proposes an online rapid mesh segmentation method based on an online sequential extreme learning machine (OS-ELM). In the training stage, the OS-ELM is trained by analyzing the mapping relationship between the shape descriptors of the mesh and the Gaussian curvature threshold. We reduce the dimensionality of the shape descriptor vector via principal component analysis (PCA) and extract the Gaussian curvature threshold of the mesh as the sample label using statistics. In the segmentation stage, the Gaussian curvature threshold is quickly extracted to realize the online rapid mesh segmentation via the OS-ELM. Simultaneously, the OS-ELM is updated to realize online incremental learning based on a small number of training samples. Our method is verified using the meshes provided from ShapeNetCore. The experimental results indicate that segmentation results similar to manual segmentation can be rapidly generated online using our method. INDEX TERMS Online sequential extreme learning machine, rapid mesh segmentation, incremental learning, Gaussian curvature threshold, web environment.

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Section: Other Methodsmentioning

confidence: 99%

An Online Rapid Mesh Segmentation Method Based on an Online Sequential Extreme Learning Machine

et al. 2019

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“…The scanned mesh is closed and therefore accepts no (bijective) parameterization. The closed mesh is segmented into quasi-developable meshes using a heat-based segmentation approach [31]. Figure 14 plots the parameterization results for the segmented mesh.…”

Section: Reverse Engineering Of Cow Vertebramentioning

confidence: 99%

“…Figure 14 plots the parameterization results for the segmented mesh. Each submesh bijective parameterization presents low distortion, enabling further reverse engineering operations such as NURBs reparameterization [14], finite element analysis, structural optimization, and/or dimensional inspection [31].…”

Section: Reverse Engineering Of Cow Vertebramentioning

confidence: 99%

Quasi-Isometric Mesh Parameterization Using Heat-Based Geodesics and Poisson Surface Fills

et al. 2019

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In the context of CAD, CAM, CAE, and reverse engineering, the problem of mesh parameterization is a central process. Mesh parameterization implies the computation of a bijective map ϕ from the original mesh M ∈ R 3 to the planar domain ϕ ( M ) ∈ R 2 . The mapping may preserve angles, areas, or distances. Distance-preserving parameterizations (i.e., isometries) are obviously attractive. However, geodesic-based isometries present limitations when the mesh has concave or disconnected boundary (i.e., holes). Recent advances in computing geodesic maps using the heat equation in 2-manifolds motivate us to revisit mesh parameterization with geodesic maps. We devise a Poisson surface underlying, extending, and filling the holes of the mesh M. We compute a near-isometric mapping for quasi-developable meshes by using geodesic maps based on heat propagation. Our method: (1) Precomputes a set of temperature maps (heat kernels) on the mesh; (2) estimates the geodesic distances along the piecewise linear surface by using the temperature maps; and (3) uses multidimensional scaling (MDS) to acquire the 2D coordinates that minimize the difference between geodesic distances on M and Euclidean distances on R 2 . This novel heat-geodesic parameterization is successfully tested with several concave and/or punctured surfaces, obtaining bijective low-distortion parameterizations. Failures are registered in nonsegmented, highly nondevelopable meshes (such as seam meshes). These cases are the goal of future endeavors.

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“…Segmentation of geometry is one of the basic requirements for reverse engineering, shape optimization, and shape synthesis. There are successful algorithms for segmenting 3D geometry (3D point cloud / triangulated surface) [1,2,3], and successful algorithms for segmenting 2D images [4,5,6,7,8]. Algorithms for both dimensions, 2D and 3D, are based on the same ideas/approaches.…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation in Shape Synthesis, Shape Optimization, And Reverse Engineering

Ćurković¹,

Ćurković²,

Vučina³

et al. 2022

Artificial Intelligence and Applications

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Image segmentation and segmentation of geometry are one of the basic requirements for reverse engineering, shape synthesis, and shape optimization. In terms of shape optimization and shape synthesis where the original geometry should be faithfully replaced with some mathematical parametric model (NURBS, hierarchical NURBS, T-Spline, …) segmentation of geometry may be done directly on 3D geometry and its corresponding parametric values in the 2D parametric domain. In our approach, we are focused on segmentation of 2D parametric domain as an image instead of 3D geometry. The reason for this lies in our dynamic hierarchical parametric model, which controls the results of various operators from image processing applied to the parametric domain.

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Hybrid geometry / topology based mesh segmentation for reverse engineering

Cited by 9 publications

References 41 publications

An Online Rapid Mesh Segmentation Method Based on an Online Sequential Extreme Learning Machine

An Online Rapid Mesh Segmentation Method Based on an Online Sequential Extreme Learning Machine

Quasi-Isometric Mesh Parameterization Using Heat-Based Geodesics and Poisson Surface Fills

Image Segmentation in Shape Synthesis, Shape Optimization, And Reverse Engineering

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