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首页> 外文会议>IEEE International Conference on High Performance Computing and Communications;IEEE International Conference on Smart City;IEEE International Conference on Data Science and Systems >Integrated Quality Mesh Generation for Poisson Surface Reconstruction in HPC Applications
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Integrated Quality Mesh Generation for Poisson Surface Reconstruction in HPC Applications

机译：在HPC应用中用于Poisson表面重建的集成质量网格生成

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Finite Element Analysis (FEA) is a numerical method for solving engineering problem. Implementations of FEA are common high performance computing (HPC) applications. As a typical application of FEA, surface reconstruction of large model, proves to be a computationally expensive task as well. Poisson Surface Reconstruction, a cutting-edge surface reconstruction algorithm, is a commonly used method for efficiently solving linear systems and it creates watertight surfaces from oriented point sets. Poisson Surface Reconstruction leverages an octree-based Marching Cubes (MC) method for isosurface extraction. Hierarchical octree structure avoids unnecessary cells visiting and prevents cracks arising. In this paper, we integrate several quality improved methods with MC and coordinate unrelated components, obtaining a better quality mesh implementation. We improve mesh quality based on an extended lookup table and modify the connectivity of some fundamental patterns in MC, which effectively remove the reconstruction holes, thus improving overall surface quality. As for the relative value between each vertex and the average isovalue, the extended table explicitly differentiates between “strictly larger” and “equal to”. Newly introduced patterns in MC statistically prevent poor quality triangles production. Moreover, a decision making algorithm is proposed to eliminate ambiguity problems. We adapt SnapMC algorithm to avoid non manifold triangles to a certain extent. Comparisons with traditional Poisson algorithm and Smooth Signed Distance (SSD) highlight the capability in quality mesh generation and efficacy in handling high computational demand.
机译：有限元分析（FEA）是解决工程问题的一种数值方法。 FEA的实现是常见的高性能计算（HPC）应用程序。作为有限元分析的典型应用，大型模型的曲面重建也被证明是一项计算量巨大的任务。泊松曲面重建（Poisson Surface Reconstruction）是一种先进的曲面重建算法，是有效求解线性系统的常用方法，它可以根据定向点集创建水密曲面。泊松曲面重建利用基于八叉树的Marching Cubes（MC）方法进行等值面提取。分层八叉树结构可避免不必要的单元访问，并防止出现裂缝。在本文中，我们将几种质量改进的方法与MC集成在一起，并协调不相关的组件，从而获得质量更高的网格实现。我们基于扩展的查找表提高了网格质量，并修改了MC中一些基本图案的连通性，从而有效地消除了重建孔，从而提高了整体表面质量。至于每个顶点与平均等值之间的相对值，扩展表明确区分“严格大于”和“等于”。 MC中新引入的模式从统计学上防止了不良三角形的产生。此外，提出了一种决策算法来消除歧义问题。我们采用SnapMC算法在一定程度上避免非流形三角形。与传统泊松算法和平滑签名距离（SSD）的比较突出了高质量网格生成的能力以及处理高计算需求的功效。

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