CT scans reveal hidden paths of yarn inside crocheted objects
CT2Yarn: Yarn-Level Reconstruction of Crochet from Computed Tomography
Graphics
Summary
Crochet involves looping one continuous yarn to make complex 3D shapes, but figuring out the exact path of the yarn inside is very hard because the loops cover each other. The authors designed a method called CT2Yarn that uses detailed CT scans to see inside the crochet and find the yarn’s path. They analyze tiny fiber directions and combine this info to map out the full yarn path, with some help from users when things are unclear. This helps future work like simulating how the yarn moves or showing realistic images of crochet.
crochetyarnmicro-computed tomographyfiber orientationanisotropic mean-shifttopology skeletonizationpoint cloudloop detectionphysically based simulationply
Authors
Chang Luo, Nobuyuki Umetani
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.