  Hand pose estimation from monocular depth images has been an important and
challenging problem in the Computer Vision community. In this paper, we present
a novel approach to estimate 3D hand joint locations from 2D depth images.
Unlike most of the previous methods, our model captures the 3D spatial
information from a depth image thereby giving it a greater understanding of the
input. We voxelize the input depth map to capture the 3D features of the input
and perform 3D data augmentations to make our network robust to real-world
images. Our network is trained in an end-to-end manner which reduces time and
space complexity significantly when compared to other methods. Through
extensive experiments, we show that our model outperforms state-of-the-art
methods with respect to the time it takes to train and predict 3D hand joint
locations. This makes our method more suitable for real-world hand pose
estimation scenarios.
