In this paper we give a new, efficient algorithm for computing curve skeletons, based on local separators. Our efficiency stems from a multilevel approach, where we solve small problems across levels of detail and combine these in order to quickly obtain a skeleton. We do this in a highly modular fashion, ensuring complete flexibility in adapting the algorithm for specific types of input or for otherwise targeting specific applications. Separator based skeletonization was first proposed by B{\ae}rentzen and Rotenberg in [ACM Tran. Graphics'21], showing high quality output at the cost of running times which become prohibitive for large inputs. Our new approach retains the high quality output, and applicability to any spatially embedded graph, while being orders of magnitude faster for all practical purposes. We test our skeletonization algorithm for efficiency and quality in practice, comparing it to local separator skeletonization on the University of Groningen Skeletonization Benchmark [Telea'16].

翻译：本文提出一种基于局部分隔子的高效曲线骨架计算新算法。其效率源于多层级方法：通过在不同细节层级上求解小规模问题，并整合这些结果以快速获得骨架。我们以高度模块化的方式实现该过程，确保算法可灵活适应特定输入类型或针对具体应用场景进行定制。基于分隔子的骨架化方法最早由Bærentzen与Rotenberg在[ACM Tran. Graphics'21]中提出，该方法虽能生成高质量输出，但处理大规模输入时运行时间过长。本算法在保留高质量输出及适用于任意空间嵌入图的基础上，实际应用中实现了数量级的性能提升。我们在格罗宁根大学骨架化基准测试集[Telea'16]上，通过与局部分隔子骨架化方法进行对比，从效率与质量两方面验证了所提算法的实用性。