Measure2Shape: A novel footwear customisation framework utilising 3D shape estimation from anthropometric measurements with an orthosis case study

Abstract

To address the limitations of relying on expensive 3D scanners for obtaining foot data in footwear customisation, this paper introduces a novel framework, Measure2Shape, which estimates 3D foot shapes using anthropometric measurement data. To achieve this, we established a large-scale 3D foot dataset with measurement data and created statistical shape models (SSMs) to represent the range of foot variations. We then proposed efficient forward- and backward-search algorithms to accurately determine the regression matrix, which connects the optimal combination of 3D measurements to the SSM coefficients of the 3D foot shape. Compared to existing 3D foot model estimation methods, our approach achieves high-precision 3D foot shape predictions using fewer dimensional measurements, with the optimal number being 6 and an average prediction error of 2.49 (±0.75) mm. Additionally, orthosis designed based on the predicted 3D foot model effectively reduce both static and dynamic peak plantar pressures, validating the reliability of our model. More importantly, the proposed regression search method can be extended to 3D estimations for other body regions, offering a wide range of customisation solutions beyond footwear. In the future, we will further expand the dataset to build a more robust 3D foot prediction model. Our project will be publicly available at: https://github.com/Easy-Shu/Measure2Shape.