Abstract
Accurate hand measurement data is of crucial importance in medical science, fashion industry, and augmented/virtual reality applications. Conventional methods extract the hand measurements manually using a measuring tape, thereby being very time-consuming and yielding unreliable measurements. In this paper, we propose–to the best of our knowledge–the first deep-learning-based method to automatically measure the hand in a non-contact manner from a single 3D hand scan. The proposed method employs a 3D hand scan, extracts the features, reconstructs the hand by making use of a 3D hand template, transfers the measurements defined on the template and extracts them from the reconstructed hand. In order to train, validate, and test the method, a novel large-scale synthetic hand dataset is generated. The results on both the unseen synthetic data and the unseen real scans captured by the Occipital structure sensor Mark I demonstrate that the proposed method outperforms the state-of-the-art method in most hand measurement types.
Pengpeng Hu
Assistant Professor
Pengpeng Hu is currently an Assistant Professor with the Center for Computational Science and Mathematical Modeling, Coventry University, Coventry, U.K. He was a Senior Researcher with the Department of Electronics and Informatics, Vrije Universiteit Brussel (VUB), Brussels, Belgium. In 2016, he was a Visiting Scholar with the School of Informatics, Edinburgh University, Edinburgh, U.K. In 2017, he was a Post-Doctoral Fellow with the Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, U.K. Since 2018, he has been with VUB. His current research interests include biometrics, geometric deep learning, 3-D human body reconstruction, point cloud processing, and measurement.