A Deep-Learning-Based Approach to Automatically Measuring Foots From a 3D Scan

The human foot is an incredibly complex combination of bones, joints, and muscles which helps humans with their balance, posture, and mobility. Foot measurement extraction plays an essential role in many applications ranging from medical science to fashion industry. Conventional foot measurement extraction methods require manual interventions using a measuring tape or its digital twins. Recent advancements in 3D scanning technologies and deep learning strategies enable us to propose, to the best of our knowledge, the first deep-learning-based approach to automatic foot measurement extraction from a single 3D scan. The proposed method involves three steps: (i) 3D foot data acquisition, (ii) template fitting, and (iii) measurement extraction. The foot is scanned with the Occipital Structure Sensor Pro. The template fitting process is performed using a deep neural network trained for foot template fitting. Finally, the measurement defined on the template are transferred and refined to the fitted template. The template fitting method is trained on a novel large dataset of dense synthetic foot samples. The experimental measurement results demonstrate that the proposed method performs well on both unseen synthetic and real scans.