Automatic and Fast Extraction of 3D Hand Measurements using a Deep Neural Network

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Recent advancements in 3D scanning technologies enable us to acquire the hand geometry represented as a threedimensional point cloud. Providing accurate 3D hand scanning and accurately extracting its biometrics are of crucial importance
for a number of applications in medical sciences, fashion industry, augmented and virtual reality (AR/VR). Traditional methods for hand measurement extraction require manual intervention using a measuring tape, which is time-consuming and highly dependent on the operator’s expertise. In this paper, we propose, to the best of our knowledge, the first deep neural network for automatic hand measurement extraction from a single 3D scan (H-Net). The proposed network follows an encoder-decoder architecture design, taking a point cloud of the hand as input and outputting the reconstructed hand mesh as well as the corresponding measurement values. In order to train the proposed deep model, a novel synthetic dataset of hands in various shapes and poses and their corresponding measurements is proposed. Experimental results on both synthetic data and real scans captured by Occipital Mark I structure sensor demonstrate that the proposed method outperforms the state-of-the-art methods in terms of accuracy and speed.
Original languageEnglish
Title of host publication2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
Number of pages6
ISBN (Electronic)978-1-6654-8360-5
ISBN (Print)978-1-6654-8361-2
Publication statusPublished - 16 May 2022

Publication series

NameConference Record - IEEE Instrumentation and Measurement Technology Conference
ISSN (Print)1091-5281

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Copyright 2022 Elsevier B.V., All rights reserved.


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