Accurate 3D foot reconstruction is crucial for personalized orthotics, digital healthcare, and virtual fittings. However, existing methods struggle with incomplete scans and anatomical variations, particularly in self-scanning scenarios where user mobility is limited, making it difficult to capture areas like the arch and heel. We present a novel end-to-end pipeline that refines Structure-from-Motion (SfM) reconstruction. It first resolves scan alignment ambiguities using SE(3) canonicalization with a viewpoint prediction module, then completes missing geometry through an attention-based network trained on synthetically augmented point clouds. Our approach achieves state-of-the-art performance on reconstruction metrics while preserving clinically validated anatomical fidelity. By combining synthetic training data with learned geometric priors, we enable robust foot reconstruction under real-world capture conditions, unlocking new opportunities for mobile-based 3D scanning in healthcare and retail.
View on arXiv@article{fogarty2025_2502.20511,
title={ Best Foot Forward: Robust Foot Reconstruction in-the-wild },
author={ Kyle Fogarty and Jing Yang and Chayan Kumar Patodi and Aadi Bhanti and Steven Chacko and Cengiz Oztireli and Ujwal Bonde },
journal={arXiv preprint arXiv:2502.20511},
year={ 2025 }
}