CT2Yarn: Yarn-Level Reconstruction of Crochet from Computed Tomography

📅 2026-09-07
📈 Citations: 0
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🤖 AI Summary
研究通过CT2Yarn框架,利用微CT扫描和人机交互方法解决从真实钩织物中恢复连续纱线路径的问题。
📝 Abstract
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.
Problem

Research questions and friction points this paper is trying to address.

yarn path
micro-CT
self-occlusion
crochet
fiber orientation
Innovation

Methods, ideas, or system contributions that make the work stand out.

CT2Yarn
Gabor filtering
anisotropic mean-shift
topology skeletonization
interactive user interface
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