دانلود رایگان مقاله مش بندی چهار ضلعی با روش نمونه گیری حداکثر پواسون دیسک

عنوان فارسی
مش بندی چهار ضلعی از طریق روش نمونه گیری حداکثر پواسون دیسک
عنوان انگلیسی
Tetrahedral meshing via maximal Poisson-disk sampling
صفحات مقاله فارسی
0
صفحات مقاله انگلیسی
14
سال انتشار
2016
نشریه
الزویر - Elsevier
فرمت مقاله انگلیسی
PDF
کد محصول
E572
رشته های مرتبط با این مقاله
ریاضی
گرایش های مرتبط با این مقاله
آمار ریاضی
مجله
طراحی هندسی به کمک کامپیوتر - Computer Aided Geometric Design
دانشگاه
آزمایشگاه ملی تشخیص الگو، موسسه اتوماسیون، آکادمی علوم چین
کلمات کلیدی
تولید مش چهار ضلعی، حداکثر پواسون دیسک روش نمونه گیری، حذف برش، بهینه سازی مش
چکیده

Abstract


In this paper, we propose a simple yet effective method to generate 3D-conforming tetrahedral meshes from closed 2-manifold surfaces. Our approach is inspired by recent work on maximal Poisson-disk sampling (MPS), which can generate well-distributed point sets in arbitrary domains. We first perform MPS on the boundary of the input domain, we then sample the interior of the domain, and we finally extract the tetrahedral mesh from the samples by using 3D Delaunay or regular triangulation for uniform or adaptive sampling, respectively. We also propose an efficient optimization strategy to protect the domain boundaries and to remove slivers to improve the meshing quality. We present various experimental results to illustrate the efficiency and the robustness of our proposed approach. We demonstrate that the performance and quality (e.g., minimal dihedral angle) of our approach are superior to current state-of-the-art optimization-based approaches.

نتیجه گیری

6. Conclusion


In this paper, we introduced a simple yet efficient method for high-quality tetrahedral mesh generation that is superior to the current state-of-the-art approaches. Our approach is based on the maximal Poisson-disk sampling framework, while taking boundary preservation and mesh grading into account. Several optimization steps are performed to greatly improve the meshing quality. We demonstrated the validity of our algorithm by meshing a variety of complex domains even with sharp features, as well as comparing it with the state-of-the-art approaches. In the future, we plan to extend our approach to anisotropic mesh generation. We are also interested in meshing implicit surfaces such as isosurfaces.


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