Abstract We present co-skeletons, a new method that computes consistent curve skeletons for 3D shapes from a given family. We compute co-skeletons in terms of sampling density and semantic relevance, while preserving the desired characteristics of traditional, per-shape curve skeletonization approaches. We take the curve skeletons extracted by traditional approaches for all shapes from a family as input, and compute semantic correlation information of individual skeleton branches to guide an edge-pruning process via skeleton-based descriptors, clustering, and a voting algorithm. Our approach achieves more concise and family-consistent skeletons when compared to traditional per-shape methods. We show the utility of our method by using co-skeletons for shape segmentation and shape blending on real-world data.

中文翻译：

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共同骨架：形状族的一致曲线骨架

摘要 我们提出了共同骨架，这是一种新方法，可以为给定系列的 3D 形状计算一致的曲线骨架。我们根据采样密度和语义相关性计算共同骨架，同时保留传统的按形状曲线骨架化方法的所需特征。我们将通过传统方法提取的所有形状的曲线骨架作为输入，计算各个骨架分支的语义相关信息，通过基于骨架的描述符、聚类和投票算法来指导边缘修剪过程。与传统的 per-shape 方法相比，我们的方法实现了更简洁和家庭一致的骨架。我们通过使用共同骨架对现实世界数据进行形状分割和形状混合来展示我们方法的实用性。