  Articulated hand pose estimation plays an important role in human-computer
interaction. Despite the recent progress, the accuracy of existing methods is
still not satisfactory, partially due to the difficulty of embedded
high-dimensional and non-linear regression problem. Different from the existing
discriminative methods that regress for the hand pose with a single depth
image, we propose to first project the query depth image onto three orthogonal
planes and utilize these multi-view projections to regress for 2D heat-maps
which estimate the joint positions on each plane. These multi-view heat-maps
are then fused to produce final 3D hand pose estimation with learned pose
priors. Experiments show that the proposed method largely outperforms
state-of-the-art on a challenging dataset. Moreover, a cross-dataset experiment
also demonstrates the good generalization ability of the proposed method.
