  Recently, 3D input data based hand pose estimation methods have shown
state-of-the-art performance, because 3D data capture more spatial information
than the depth image. Whereas 3D voxel-based methods need a large amount of
memory, PointNet based methods need tedious preprocessing steps such as
K-nearest neighbour search for each point. In this paper, we present a novel
deep learning hand pose estimation method for an unordered point cloud. Our
method takes 1024 3D points as input and does not require additional
information. We use Permutation Equivariant Layer (PEL) as the basic element,
where a residual network version of PEL is proposed for the hand pose
estimation task. Furthermore, we propose a voting based scheme to merge
information from individual points to the final pose output. In addition to the
pose estimation task, the voting-based scheme can also provide point cloud
segmentation result without ground-truth for segmentation. We evaluate our
method on both NYU dataset and the Hands2017Challenge dataset. Our method
outperforms recent state-of-the-art methods, where our pose accuracy is
currently the best for the Hands2017Challenge dataset.
