Restoring, estimating the fully 3D hand skeleton and pose from the image data of the captured sensors/cameras applied in many applications of computer vision and robotics: human-computer interaction; gesture recognition, interactive games, Computer-Aided Design (CAD), sign languages, action recognition, etc. These are applications that flourish in Virtual Reality and Augmented Reality (VR/AR) technologies. Previous survey studies focused on analyzing methods to solve the relational problems of

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... and estimation in the 2D and 3D space: Hand pose estimation, hand parsing, fingertip detection; List methods, data collection technologies, datasets of 3D hand pose estimation. In this paper, we surveyed studies in which Convolutional Neural Networks (CNNs) were used to estimate the 3D hand pose from data obtained from the cameras (e.g., RGB camera, depth(D) camera, RGB-D camera, stereo camera). The surveyed studies were divided based on the type of input data and publication time. The study discussed several areas of 3D hand pose estimation: (i)the number of valuable studies about 3D hand pose estimation, (ii) estimates of 3D hand pose when using 3D CNNs and 2D CNNs, (iii) challenges of the datasets collected from egocentric vision sensors, and (iv) methods used to collect and annotate datasets from egocentric vision sensors. The estimation process followed two directions: (a) using the 2D CNNs to predict 2D hand pose, and (b) using the 3D synthetic dataset (3D annotations/ ground truth) to regress 3D hand pose or using the 3D CNNs to predict the immediacy of 3D hand pose. Our survey focused on the CNN model/architecture, the datasets, the evaluation measurements, the results of 3D hand pose estimation on the available. Lastly, we also analyze some of the challenges of estimating 3D hand pose on the egocentric vision datasets.