CT2Yarn：从计算机断层扫描中重建钩针编织的纱线级路径
CT2Yarn: Yarn-Level Reconstruction of Crochet from Computed Tomography
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中文总结 AI 辅助
CT2Yarn提出一种人在回路的框架，利用微型CT扫描和各向异性均值漂移及骨架化技术，从钩针编织物中恢复单一连续纱线路径，支持物理模拟、渲染和针法提取。
中文摘要 AI 辅助
我们提出了CT2Yarn，一种人在回路框架，用于从真实钩针编织物体的微型计算机断层扫描（micro-CT）中恢复单一连续纱线路径。钩针编织是一种通过单一纱线形成的互锁环来创建复杂三维形状的手工艺。由于严重的自遮挡，从外部观察中恢复底层纱线路径具有挑战性。虽然微型CT揭示了钩针编织物体的完整内部结构，但体积扫描本身并未明确编码纱线如何穿越物体。这一挑战源于纱线的层级结构：一根纱线由多股加捻的股线组成，而每一股本身又由加捻的纤维组成。因此，在微型CT扫描中观察到的局部纤维方向与整体纱线方向不一致。为了从股线级纤维方向恢复纱线级方向，我们首先使用Gabor滤波估计局部纤维方向，并将体积转换为定向点云。然后，我们引入一种各向异性均值漂移过程，该过程在纱线半径的邻域内聚合局部纤维方向，形成纱线级方向估计。结合自动拓扑骨架化策略，我们的方法提取纱线路径片段。随后的片段连接、交叉点清理和环检测将这些树合并为少量长曲线。在自动重建仍然模糊的情况下，基于草图的用户界面使用户能够交互式地完成单一连续纱线路径。恢复的纱线路径支持下游应用，包括基于物理的模拟、股线级渲染和针法模式提取。
英文摘要
We introduce CT2Yarn, a human-in-the-loop framework for recovering a single continuous yarn path from micro-computed tomography (micro-CT) scans of real crochet objects. Crochet is a craft that creates complex three-dimensional shapes by interlocking loops formed from a single yarn. Recovering the underlying yarn path from external observations is challenging because of severe self-occlusion. While micro-CT reveals the full internal structure of a crochet object, the volumetric scan alone does not explicitly encode how the yarn traverses the object. This challenge stems from the hierarchical structure of yarn: a yarn consists of multiple twisted plies, and each ply itself consists of twisted fibers. Consequently, local fiber orientations observed in micro-CT scans are not aligned with the overall yarn direction. To recover yarn-level orientations from ply-level fiber orientations, we first estimate local fiber directions using Gabor filtering and convert the volume into an oriented point cloud. We then introduce an anisotropic mean-shift procedure that aggregates local fiber orientations within a neighborhood of the yarn radius into yarn-level orientation estimates. Combined with an automatic topology skeletonization strategy, our method extracts yarn-path fragments. Subsequent fragment linking, junction cleaning, and loop detection merge these trees into a small number of long curves. Where the automatic reconstruction remains ambiguous, a sketch-based user interface enables users to interactively complete the single continuous yarn path. The recovered yarn path enables downstream applications including physically based simulation, ply-level rendering, and stitch-pattern extraction.
发表机构
- The University of Tokyo（东京大学）
机构由 AI 辅助整理，请以论文原文为准。
