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


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.

  • T. Chatzis, A. Stergioulas, D. Konstantinidis, K. Dimitropoulos, P. Daras, "A Comprehensive Study on Deep Learning-Based 3D Hand Pose Estimation Methods", Applied Sciences, 10(19), 6850, 2020. DOI: https://doi.org/10.3390/app10196850

  • Full document available here.
    Contact Information

    Dr. Petros Daras, Research Director
    6th km Charilaou – Thermi Rd, 57001, Thessaloniki, Greece
    P.O.Box: 60361
    Tel.: +30 2310 464160 (ext. 156)
    Fax: +30 2310 464164
    Email: daras(at)iti(dot)gr