仅用几何信息自动预测服装缝合关系，无需人工标注。
AutoSew: A Geometric Approach to Stitching Prediction with Graph Neural Networks
- 将缝合预测建模为图匹配问题，用图神经网络捕捉几何上下文。
- 在18,000+条真实服装轮廓上达到96%的F1分数，73.3%无错装配。
- 适合工业级服装自动化生产，无需语义标签或人工干预。
从缝制图纸自动完成服装组装仍面临挑战，主要因缺乏标准化标注协议及语义线索缺失。现有方法常依赖面板标签或手工规则，难以适用于现实中的非标准图纸。我们提出AutoSew，一种完全自动、基于几何的方法，直接从2D图案轮廓预测缝合对应关系。该方法将问题建模为图匹配任务，利用图神经网络捕获局部与全局几何上下文，并采用可微分最优传输求解器推断缝合关系（包括多边连接）。为支持该任务，我们更新了GarmentCodeData数据集，对超过18,000个图案添加了符合工业场景的真实多边注释。AutoSew在测试中取得96% F1分数，成功无误装配73.3%的服装，优于现有方法，且仅依赖几何输入。结果表明，仅凭几何信息即可稳健引导缝合预测，实现无需人工输入的可扩展服装组装。
原文摘要 · Abstract (English)
Automating garment assembly from sewing patterns remains a significant challenge due to the lack of standardized annotation protocols and the frequent absence of semantic cues. Existing methods often rely on panel labels or handcrafted heuristics, which limit their applicability to real-world, non-conforming patterns. We present AutoSew, a fully automatic, geometry-based approach for predicting stitch correspondences directly from 2D pattern contours. AutoSew formulates the problem as a graph matching task, leveraging a Graph Neural Network to capture local and global geometric context, and employing a differentiable optimal transport solver to infer stitching relationships-including multi-edge connections. To support this task, we update the GarmentCodeData dataset modifying over 18k patterns with realistic multi-edge annotations, reflecting industrial assembly scenarios. AutoSew achieves 96% F1-score and successfully assembles 73.3% of test garments without error, outperforming existing methods while relying solely on geometric input. Our results demonstrate that geometry alone can robustly guide stitching prediction, enabling scalable garment assembly without manual input.
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