  3D Hand pose estimation from a single depth image is an essential topic in
computer vision and human-computer interaction. Although the rising of deep
learning method boosts the accuracy a lot, the problem is still hard to solve
due to the complex structure of the human hand. Existing methods with deep
learning either lose spatial information of hand structure or lack a direct
supervision of joint coordinates. In this paper, we propose a novel Pixel-wise
Regression method, which use spatial-form representation (SFR) and
differentiable decoder (DD) to solve the two problems. To use our method, we
build a model, in which we design a particular SFR and its correlative DD which
divided the 3D joint coordinates into two parts, plane coordinates and depth
coordinates and use two modules named Plane Regression (PR) and Depth
Regression (DR) to deal with them respectively. We conduct an ablation
experiment to show the method we proposed achieve better results than the
former methods. We also make an exploration on how different training
strategies influence the learned SFRs and results. The experiment on three
public datasets demonstrates that our model is comparable with the existing
state-of-the-art models and in one of them our model can reduce mean 3D joint
error by 25%.
