Deep learning-based automated extraction of anthropometric measurements from a single 3D scan
Abstract
In this study, we propose the first approach for automatic contact-less anthropometric measurements extraction based on deep-learning (AM-DL). A novel module dubbed multiscale EdgeConv is proposed to learn local features from point clouds at multiple scales. Multiscale EdgeConv can be directly integrated with other neural networks for various tasks, e.g., classification of point clouds. We exploit this module to design an encoder–decoder architecture that learns to deform a template model to fit a given scan. The measurement values are then calculated on the deformed template model. To evaluate the proposed method, 27 female and 25 male subjects were scanned using a photogrametry-based scanner and measured by an experienced tailor.
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.