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On Volumetric Shape Reconstruction from Implicit Forms

机译:从隐式形式重构体积形状

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In this paper we report on the evaluation of volumetric shape reconstruction methods that consider as input implicit forms in 3D. Many visual applications build implicit representations of shapes that are converted into explicit shape representations using geometric tools such as the Marching Cubes algorithm. This is the case with image based reconstructions that produce point clouds from which implicit functions are computed, with for instance a Poisson reconstruction approach. While the Marching Cubes method is a versatile solution with proven efficiency, alternative solutions exist with different and complementary properties that are of interest for shape modeling. In this paper, we propose a novel strategy that builds on Centroidal Voronoi Tessellations (CVTs). These tessellations provide volumetric and surface representations with strong regularities in addition to provably more accurate approximations of the implicit forms considered. In order to compare the existing strategies, we present an extensive evaluation that analyzes various properties of the main strategies for implicit to explicit volumetric conversions: Marching cubes, Delaunay refinement and CVTs, including accuracy and shape quality of the resulting shape mesh.
机译:在本文中,我们报告了将体积形状重建方法视为3D输入隐式形式的评估。许多视觉应用程序会使用诸如Marching Cubes算法之类的几何工具来构建形状的隐式表示,然后将其转换为显式的形状表示。基于图像的重建就是这种情况,该重建产生点云,并使用泊松重建方法从中计算隐函数。尽管Marching Cubes方法是一种行之有效的通用解决方案,但存在具有不同和互补特性的替代解决方案,这对于形状建模很重要。在本文中,我们提出了一种基于质心Voronoi镶嵌(CVT)的新颖策略。这些棋盘格不仅为所考虑的隐式提供了更为精确的近似值,还为体积和表面表示提供了强大的规律性。为了比较现有策略,我们提供了一个广泛的评估,该评估分析了隐式到显式体积转换的主要策略的各种属性:行进立方体,Delaunay细化和CVT,包括所得形状网格的精度和形状质量。

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