  Hand pose estimation from monocular depth images is an important and
challenging problem for human-computer interaction. Recently deep convolutional
networks (ConvNet) with sophisticated design have been employed to address it,
but the improvement over traditional methods is not so apparent. To promote the
performance of directly 3D coordinate regression, we propose a tree-structured
Region Ensemble Network (REN), which partitions the convolution outputs into
regions and integrates the results from multiple regressors on each regions.
Compared with multi-model ensemble, our model is completely end-to-end
training. The experimental results demonstrate that our approach achieves the
best performance among state-of-the-arts on two public datasets.
