ENIGMA: evolutionary non-isometric geometry MAtching

M. Edelstein, Danielle Ezuz, M. Ben-Chen
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引用次数: 12

Abstract

In this paper we propose a fully automatic method for shape correspondence that is widely applicable, and especially effective for non isometric shapes and shapes of different topology. We observe that fully-automatic shape correspondence can be decomposed as a hybrid discrete/continuous optimization problem, and we find the best sparse landmark correspondence, whose sparse-to-dense extension minimizes a local metric distortion. To tackle the combinatorial task of landmark correspondence we use an evolutionary genetic algorithm, where the local distortion of the sparse-to-dense extension is used as the objective function. We design novel geometrically guided genetic operators, which, when combined with our objective, are highly effective for non isometric shape matching. Our method outperforms state of the art methods for automatic shape correspondence both quantitatively and qualitatively on challenging datasets.
进化非等距几何匹配
本文提出了一种广泛适用的全自动形状对应方法,特别适用于非等距形状和不同拓扑形状。我们发现全自动形状对应可以分解为一个离散/连续混合优化问题,并且我们找到了最佳的稀疏地标对应,其稀疏到密集的扩展使局部度量失真最小化。为了解决地标对应的组合任务,我们使用进化遗传算法,其中使用稀疏到密集扩展的局部失真作为目标函数。我们设计了一种新的几何引导遗传算子,当它与我们的目标相结合时,它对非等距形状匹配是非常有效的。在具有挑战性的数据集上,我们的方法在定量和定性上都优于自动形状对应的最先进方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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