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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Computing the Inner Distances of Volumetric Models for Articulated Shape Description with a Visibility Graph
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Computing the Inner Distances of Volumetric Models for Articulated Shape Description with a Visibility Graph

机译:使用可见度图计算体积模型的内部距离以进行关节形状描述

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摘要

A new visibility graph-based algorithm is presented for computing the inner distances of a 3D shape represented by a volumetric model. The inner distance is defined as the length of the shortest path between landmark points within the shape. The inner distance is robust to articulation and can reflect the deformation of a shape structure well without an explicit decomposition. Our method is based on the visibility graph approach. To check the visibility between pairwise points, we propose a novel, fast, and robust visibility checking algorithm based on a clustering technique which operates directly on the volumetric model without any surface reconstruction procedure, where an octree is used for accelerating the computation. The inner distance can be used as a replacement for other distance measures to build a more accurate description for complex shapes, especially for those with articulated parts. The binary executable program for the Windows platform is available from https://engineering.purdue.edu/PRECISE/VMID.
机译:提出了一种基于可见度图的新算法,用于计算由体积模型表示的3D形状的内部距离。内部距离定义为形状内界标点之间的最短路径的长度。内部距离对于关节运动是鲁棒的,并且可以很好地反映形状结构的变形,而无需明确分解。我们的方法基于可见性图方法。为了检查成对点之间的可见性,我们提出了一种基于聚类技术的新颖,快速且鲁棒的可见性检查算法,该算法可直接在体积模型上运行,而无需任何曲面重建程序,其中使用八叉树来加速计算。内部距离可以用作其他距离度量的替代,从而为复杂形状(尤其是具有铰接零件的形状)建立更准确的描述。可从https://engineering.purdue.edu/PRECISE/VMID获得Windows平台的二进制可执行程序。

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