A Data-Driven Parametric Framework for Size-Adaptive Shoe Insole Outline Generation

초록

With the rapid expansion of AR/VR-based digital platforms, there is an increasing demand for the automated generation of size-varied 3D shoe assets. However, conventional CAD-based linear scaling and PCA-based global statistical shape models are limited in their ability to capture the non-uniform and locally non-linear deformations observed in insole contours. This study proposes a size-adaptive insole contour generation framework that integrates image-based contour extraction, B-spline parametric representation, and type-specific SVR-based local displacement regression. By decomposing control-point displacements into tangent–normal components, the proposed method directly models non-linear curvature variations associated with size progression without relying on dimensionality reduction. Quantitative evaluations under an eight-fold leave-one-insole-out (LOIO) protocol show that the proposed method achieves a mean Hausdorff distance of 4.73 mm, a Chamfer distance of 1.76 mm, and an IoU of 0.929. It significantly outperforms the no-clustering ablation in both the Hausdorff distance (6.13 mm; (Formula presented.)) and IoU ((Formula presented.)), as well as the PCA-based kernel ridge regression baseline across all three metrics ((Formula presented.)). No statistically significant differences were observed between the proposed method and the ratio scaling, Gaussian process, or thin plate spline baselines ((Formula presented.)). PCA-Linear showed a small numerical advantage in the Hausdorff distance (4.16 mm), but the difference was not statistically significant ((Formula presented.)). A sensitivity analysis further reveals the existence of a practical control-point density region that balances geometric fidelity and model complexity. This work provides a data-driven parametric approach for standard size-based digital grading automation and establishes a technical foundation for future extension to full 3D shoe mesh generation. © 2026 by the authors.

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