Abstract
Accurately reconstructing 3-D hand shapes of patients is important for immobilization device customization, artificial limb generation, and hand disease diagnosis. Traditional 3-D hand scanning requires multiple scans taken around the hand with a 3-D scanning device. These methods require the patients to keep an open-palm posture during scanning, which is painful or even impossible for patients with impaired hand functions. Once multi-view partial point clouds are collected, expensive post-processing is necessary to generate a high-fidelity hand shape. To address these limitations, we propose a novel deep-learning method dubbed PatientHandNet to reconstruct high-fidelity hand shapes in a canonical open-palm pose from multiple-depth images acquired with a single-depth camera.
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.