Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106412
Title: Real-time 3D hand pose estimation with 3D convolutional neural networks
Authors: Ge, Liuhao
Liang, Hui
Yuan, Junsong
Thalmann, Daniel
Keywords: 3D Convolutional Neural Networks
DRNTU::Engineering::Electrical and electronic engineering
3D Hand Pose Estimation
Issue Date: 2018
Source: Ge, L., Liang, H., Yuan, J., & Thalmann, D. (2019). Real-Time 3D Hand Pose Estimation with 3D Convolutional Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(4), 956-970. doi:10.1109/TPAMI.2018.2827052
Series/Report no.: IEEE Transactions on Pattern Analysis and Machine Intelligence
Abstract: In this paper, we present a novel method for real-time 3D hand pose estimation from single depth images using 3D Convolutional Neural Networks (CNNs). Image-based features extracted by 2D CNNs are not directly suitable for 3D hand pose estimation due to the lack of 3D spatial information. Our proposed 3D CNN-based method, taking a 3D volumetric representation of the hand depth image as input and extracting 3D features from the volumetric input, can capture the 3D spatial structure of the hand and accurately regress full 3D hand pose in a single pass. In order to make the 3D CNN robust to variations in hand sizes and global orientations, we perform 3D data augmentation on the training data. To further improve the estimation accuracy, we propose applying the 3D deep network architectures and leveraging the complete hand surface as intermediate supervision for learning 3D hand pose from depth images. Extensive experiments on three challenging datasets demonstrate that our proposed approach outperforms baselines and state-of-the-art methods. A cross-dataset experiment also shows that our method has good generalization ability. Furthermore, our method is fast as our implementation runs at over 91 frames per second on a standard computer with a single GPU.
URI: https://hdl.handle.net/10356/106412
http://hdl.handle.net/10220/47912
ISSN: 0162-8828
DOI: 10.1109/TPAMI.2018.2827052
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TPAMI.2018.2827052
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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