Kyle Fogarty1, Jing Yang1, Chayan Kumar Patodi2, Jack Foster2, Aadi Bhanti2, Steven Chacko2, Cengiz Oztireli1, Ujwal Bonde2
1University of Cambridge, 2Hike Medical
High-fidelity 3D foot reconstruction is crucial for prescription orthotics but is hindered by expensive, specialized equipment that limits patient access. We overcome this barrier with the first end-to-end pipeline to reconstruct clinically-accurate foot meshes from simple, self-captured smartphone videos. Our method uniquely solves the core challenges of in-the-wild scanning: we resolve pose ambiguities using SE (3) canonicalization with viewpoint prediction, and then complete partial geometry using an attention-based network. Clinical validation demonstrates that our reconstructions achieve state-of-the-art accuracy and meet prescription-readiness standards, preserving the anatomical fidelity essential for medical intervention. By democratizing high-quality foot assessment, our work unlocks new opportunities for accessible telemedicine, preventative diabetic care, and personalized orthotic treatment.
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