Spend Bits Where the Pattern Bends: Corner-Based Coding of Sewing-Pattern Boundaries

Learned models increasingly read and write sewing patterns, and every panel passes through them as a coded boundary that pattern software must accept back. Current boundary codes cut the panel into equal pieces and give each the same number of bits. A straight seam then costs as much as an armhole, and the corner between them gets rounded off. We observe that most of the error sits in a few short curves, and that the corners and straight seams that make up most of a panel are charged like curves. To this end, we propose CornerMCQ, a boundary code that spends bits only where the boundary bends and needs no neural network. Specifically, it stores the detected corners on a vertex grid, gives the straight runsbetween them no curve bits, and describes each remaining curve relative to its chord with a few small codebooks fitted once. On a public corpus and a private industrial archive, CornerMCQ is more accurate than Bézier fitting, PCA, and a learned vector quantizer at equal bits and several times more accurate than the same codebooks on equal pieces; on the public corpus, with fewer bits than the equal-piece code, three panels in four stay within two millimetres of the reference boundary everywhere, against one panel in a hundred.