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Open AccessFeature PaperArticle

A Comprehensive Study on Deep Learning-Based 3D Hand Pose Estimation Methods

Visual Computing Lab at Information Technologies Institute of Centre for Reseach and Technology Hellas, VCL of CERTH/ITI Hellas, 57001 Thessaloniki, Greece
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These authors contributed equally to this work.
Appl. Sci. 2020, 10(19), 6850; https://doi.org/10.3390/app10196850
Received: 8 September 2020 / Revised: 23 September 2020 / Accepted: 25 September 2020 / Published: 30 September 2020
(This article belongs to the Special Issue Computer Graphics and Virtual Reality)
The field of 3D hand pose estimation has been gaining a lot of attention recently, due to its significance in several applications that require human-computer interaction (HCI). The utilization of technological advances, such as cost-efficient depth cameras coupled with the explosive progress of Deep Neural Networks (DNNs), has led to a significant boost in the development of robust markerless 3D hand pose estimation methods. Nonetheless, finger occlusions and rapid motions still pose significant challenges to the accuracy of such methods. In this survey, we provide a comprehensive study of the most representative deep learning-based methods in literature and propose a new taxonomy heavily based on the input data modality, being RGB, depth, or multimodal information. Finally, we demonstrate results on the most popular RGB and depth-based datasets and discuss potential research directions in this rapidly growing field. View Full-Text
Keywords: computer vision; deep learning; neural networks; 3D hand pose estimation computer vision; deep learning; neural networks; 3D hand pose estimation
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Chatzis, T.; Stergioulas, A.; Konstantinidis, D.; Dimitropoulos, K.; Daras, P. A Comprehensive Study on Deep Learning-Based 3D Hand Pose Estimation Methods. Appl. Sci. 2020, 10, 6850.

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