Abstract
In this paper, a system with six depth cameras was built to scan both feet simultaneously. An improved calibration method based on a T-shaped checkerboard was used to calculate the extrinsic parameters of the cameras. T-shaped virtual checkerboards were introduced to further fine-tune the accuracy of calibration based on the iterative closest point algorithm. Based on the proposed foot scanner, a complete procedure was introduced to measure the foot automatically by locating the anatomical landmarks without manual intervention. Various experiments were presented to validate the performance of the scanner and the measurements. The results verified that the proposed methods were efficient and versatile for three-dimensional foot scanning and measurement.
Pengpeng Hu
Senior Lecturer (Associate Professor)
Pengpeng Hu is currently a Senior Lecturer (Associate Professor) with The University of Manchester. His research interests include biometrics, geometric deep learning, 3D human body reconstruction, point cloud processing, and vision-based measurement. He serves as an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Automation Science and Engineering, and Engineering and Mathematics in Medical and Life Sciences, as well as an Academic Editor for PLOS ONE and a member of the editorial board for Scientific Reports. He is also the Programme Chair for the 25th UK Workshop on Computational Intelligence (UKCI 2026) and an Area Chair for the 35th British Machine Vision Conference (BMVC 2024). He is the recipient of the Emerald Literati Award for an outstanding paper in 2019.