Efficiently computing feature-aligned and high-quality polygonal offset surfaces
•A super-linear convergent algorithm to generate a well-triangulated and feature-aligned offset surface is proposed.•We use an optimization method to keep a set of uniformly distributed sites at a specified distance away from the base surface.•Our algorithm is suitable for multiple kinds of input su...
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Veröffentlicht in: | Computers & graphics 2018-02, Vol.70, p.62-70 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | •A super-linear convergent algorithm to generate a well-triangulated and feature-aligned offset surface is proposed.•We use an optimization method to keep a set of uniformly distributed sites at a specified distance away from the base surface.•Our algorithm is suitable for multiple kinds of input surfaces such as triangle meshes, implicit functions, parametric surfaces and even point clouds.•Our algorithm has significant advantages in terms of meshing quality, computational performance, topological correctness and feature alignment.
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3D surface offsetting is a fundamental geometric operation in CAD/CAE/CAM. In this paper, we propose a super-linear convergent algorithm to generate a well-triangulated and feature-aligned offset surface based on particle system. The key idea is to distribute a set of moveable sites as uniformly as possible while keeping these sites at a specified distance away from the base surface throughout the optimization process. In order to make the final triangulation align with geometric feature lines, we use the moveable sites to predict the potential feature regions, which in turn guide the distribution of moveable sites. Our algorithm supports multiple kinds of input surfaces, e.g., triangle meshes, implicit functions, parametric surfaces and even point clouds. Compared with existing algorithms on surface offsetting, our algorithm has significant advantages in terms of meshing quality, computational performance, topological correctness and feature alignment. |
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ISSN: | 0097-8493 1873-7684 |
DOI: | 10.1016/j.cag.2017.07.003 |