Best Foot Forward: Robust Foot Reconstruction in-the-wild

• URL: http://arxiv.org/abs/2502.20511v1
• Date: Thu, 27 Feb 2025 20:40:20 GMT
• Title: Best Foot Forward: Robust Foot Reconstruction in-the-wild
• Authors: Kyle Fogarty, Jing Yang, Chayan Kumar Patodi, Aadi Bhanti, Steven Chacko, Cengiz Oztireli, Ujwal Bonde,
• Abstract summary: We present a novel end-to-end pipeline that refines Structure-from-Motion (SfM) reconstruction.<n>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.<n>Our approach achieves state-of-the-art performance on reconstruction metrics while preserving clinically validated anatomical fidelity.
• Score: 2.059210052546126
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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