  Thanks to the rapid development of CNNs and depth sensors, great progress has
been made in 3D hand pose estimation. Nevertheless, it is still far from being
solved for its cluttered circumstance and severe self-occlusion of hand. In
this paper, we propose a method that takes advantage of human hand
morphological topology (HMT) structure to improve the pose estimation
performance. The main contributions of our work can be listed as below.
Firstly, in order to extract more powerful features, we concatenate original
and last layer of initial feature extraction module to preserve hand
information better. Next, regression module inspired from hand morphological
topology is proposed. In this submodule, we design a tree-like network
structure according to hand joints distribution to make use of high order
dependency of hand joints. Lastly, we conducted sufficient ablation experiments
to verify our proposed method on each dataset. Experimental results on three
popular hand pose dataset show superior performance of our method compared with
the state-of-the-art methods. On ICVL and NYU dataset, our method outperforms
great improvement over 2D state-of-the-art methods. On MSRA dataset, our method
achieves comparable accuracy with the state-of-the-art methods. To summarize,
our method is the most efficient method which can run at 220:7 fps on a single
GPU compared with approximate accurate methods at present. The code will be
available at.
