用合成数据训练网络，实现手机自拍下的足部3D重建
Best Foot Forward: Robust Foot Reconstruction in-the-wild
- 通过视角预测模块解决扫描对齐歧义
- 在真实拍摄条件下达到领先重建精度
- 适合移动医疗与虚拟试穿场景
精准的3D足部重建对于个性化矫形器、数字医疗和虚拟试穿至关重要。然而，现有方法在不完整扫描和解剖变异方面表现不佳，尤其在用户自行扫描时，受限于移动性难以捕捉足弓和脚跟等区域。本文提出一种端到端新流程，优化结构光运动（SfM）重建：首先利用SE(3)规范化结合视角预测模块解决扫描对齐歧义，再通过基于注意力的网络，使用合成增强点云训练完成缺失几何。该方法在重建指标上达到当前最优，并保持临床验证的解剖保真度。结合合成训练数据与学习到的几何先验，实现了真实采集条件下的鲁棒足部重建，为移动3D扫描在医疗与零售领域的应用开辟新可能。
原文摘要 · Abstract (English)
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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