Automatic Foot Measurement Extraction from A 3D Point Cloud via a Deep Neural Network

The foot is a vital human body part comprising a complex system of muscles and bones sustaining the human weight, and providing balance and mobility when daily activities are being performed. Extracting accurate foot measurements is of paramount importance in many applications including medical sciences, sports and fashion industry. Traditionally, footwear brands employ contact-based foot measuring methods involving a trained operator to design and produce well-fitted footwear products. However, this process is very time consuming and is prone to human errors. With the advancement of 3D scanning technologies, the foot can be scanned accurately with an affordable 3D scanning device. In this research, we propose, to the best of our knowledge, the first deep neural network (FNet) for automatic foot measurement extraction from a 3D foot point cloud. The proposed FNet is an encoder-decoder neural network which operates on the foot point cloud and outputs the foot reconstruction as well as the corresponding measurements points utilized for measurement extraction. Our study shows that teaching the network to accurately generate the measurement points, performed with the help of the well-designed loss functions, is necessary for automatic and accurate foot measurement extraction. In order to train the proposed neural network, a large dataset of complete foot scans with their corresponding measurement points and measurement values are synthesized. The performance of the proposed method has been evaluated on both synthetic test data as well as the real scans captured by the Occipital Structure Sensor Pro. The results show that our method outperforms the state-of-the-art methods in terms of accuracy and speed.