Dual contouring of hermite data

Ju, Tao; Losasso, Frank; Schaefer, Scott; Warren, Joe

doi:10.1145/566570.566586

Cited by 390 publications

(381 citation statements)

References 27 publications

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“…Dual Contouring [31] analyzes those edges that have endpoints lying on different sides of the isosurface, called sign change edge. Each sign change edge is shared by four (uniform case) or three (adaptive case) cells, and one minimizer is calculated for each of them by minimizing a predefined Quadratic Error Function (QEF) [25].…”

Section: Quality Improvementmentioning

confidence: 99%

“…Unlike the dual contouring method [31], we use a different error function based on the function difference normalized by gradients. The function approximates the maximum difference between coarse and fine level isosurfaces to decide the level of adaptivity.…”

Section: Quality Improvementmentioning

confidence: 99%

“…By combining SurfaceNets [28] and the extended Marching Cubes algorithm [32], octree based Dual Contouring [31] can generate adaptive isosurfaces with good aspect ratio and preservation of sharp features. Elements in the extracted mesh often have bad aspect ratio.…”

Section: Previous Work Isosurface Extractionmentioning

confidence: 99%

“…Therefore we focus on the topology of an isosurface. We use the term 'dual contour' as a polygonized isosurface extracted using the dual contouring method [31]. 'Correct topology' means that an extracted dual contour is topologically equivalent to the real isosurface defined from an original input function.…”

Section: Mesh Topologymentioning

confidence: 99%

“…On the other hand, if a cube has no ambiguity, then the real isosurface is topologically equivalent to the dual contour, which is always a simple manifold. Whether a case in a cube is ambiguous or not can be checked by collapsing each edge which has two vertices with the same sign into a vertex [31]. If the cube can be collapsed into an edge, then the cube has no ambiguity and the topologically correct dual contour is generated in the cube.…”

Section: Mesh Topologymentioning

confidence: 99%

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3D finite element meshing from imaging data

Zhang

Bajaj

Sohn

2005

Computer Methods in Applied Mechanics and Engineering

166

134

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This paper describes an algorithm to extract adaptive and quality 3D meshes directly from volumetric imaging data. The extracted tetrahedral and hexahedral meshes are extensively used in the Finite Element Method (FEM). A top-down octree subdivision coupled with the dual contouring method is used to rapidly extract adaptive 3D finite element meshes with correct topology from volumetric imaging data. The edge contraction and smoothing methods are used to improve the mesh quality. The main contribution is extending the dual contouring method to crack-free interval volume 3D meshing with feature sensitive adaptation. Compared to other tetrahedral extraction methods from imaging data, our method generates adaptive and quality 3D meshes without introducing any hanging nodes. The algorithm has been successfully applied to constructing the geometric model of a biomolecule in finite element calculations.

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Section: Quality Improvementmentioning

confidence: 99%

Section: Quality Improvementmentioning

confidence: 99%

Section: Previous Work Isosurface Extractionmentioning

confidence: 99%

Section: Mesh Topologymentioning

confidence: 99%

Section: Mesh Topologymentioning

confidence: 99%

See 3 more Smart Citations

3D finite element meshing from imaging data

Zhang

Bajaj

Sohn

2005

Computer Methods in Applied Mechanics and Engineering

166

134

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A review of collision detection for deformable objects

Wang

Cao

2021

Computer Animation & Virtual

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In the process of simulating and modeling real objects, the phenomenon of objects penetrating each other may occur in the model, which is unrealistic and then the research of collision detection (CD) is generated. As a bottleneck of virtual environment simulation, researchers have conducted in-depth research on CD, especially the CD of deformable objects. In this paper, we briefly review the related research on CD of deformable objects regarding relevant literature. First, we briefly introduce previous reviews of CD. Second, we review the popular research methods and limitations of CD between deformable objects. Third, we review the popular research methods and limitations of self-collision detection in deformable objects. Finally, we discuss future directions of development.This review can be used as a reference for the application of CD in all directions.

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Geometric modeling of subcellular structures, organelles, and multiprotein complexes

Feng

Xia

Tong

et al. 2012

Numer Methods Biomed Eng

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SUMMARY Recently, the structure, function, stability, and dynamics of subcellular structures, organelles, and multi-protein complexes have emerged as a leading interest in structural biology. Geometric modeling not only provides visualizations of shapes for large biomolecular complexes but also fills the gap between structural information and theoretical modeling, and enables the understanding of function, stability, and dynamics. This paper introduces a suite of computational tools for volumetric data processing, information extraction, surface mesh rendering, geometric measurement, and curvature estimation of biomolecular complexes. Particular emphasis is given to the modeling of cryo-electron microscopy data. Lagrangian-triangle meshes are employed for the surface presentation. On the basis of this representation, algorithms are developed for surface area and surface-enclosed volume calculation, and curvature estimation. Methods for volumetric meshing have also been presented. Because the technological development in computer science and mathematics has led to multiple choices at each stage of the geometric modeling, we discuss the rationales in the design and selection of various algorithms. Analytical models are designed to test the computational accuracy and convergence of proposed algorithms. Finally, we select a set of six cryo-electron microscopy data representing typical subcellular complexes to demonstrate the efficacy of the proposed algorithms in handling biomolecular surfaces and explore their capability of geometric characterization of binding targets. This paper offers a comprehensive protocol for the geometric modeling of subcellular structures, organelles, and multiprotein complexes.

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Dual contouring of hermite data

Cited by 390 publications

References 27 publications

3D finite element meshing from imaging data

3D finite element meshing from imaging data

A review of collision detection for deformable objects

Geometric modeling of subcellular structures, organelles, and multiprotein complexes

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