  Depth-based 3D hand pose estimation is an important but challenging research
task in human-machine interaction community. Recently, dense regression methods
have attracted increasing attention in 3D hand pose estimation task, which
provide a low computational burden and high accuracy regression way by densely
regressing hand joint offset maps. However, large-scale regression offset
values are often affected by noise and outliers, leading to a significant drop
in accuracy. To tackle this, we re-formulate 3D hand pose estimation as a dense
ordinal regression problem and propose a novel Dense Ordinal Regression 3D Pose
Network (DOR3D-Net). Specifically, we first decompose offset value regression
into sub-tasks of binary classifications with ordinal constraints. Then, each
binary classifier can predict the probability of a binary spatial relationship
relative to joint, which is easier to train and yield much lower level of
noise. The estimated hand joint positions are inferred by aggregating the
ordinal regression results at local positions with a weighted sum. Furthermore,
both joint regression loss and ordinal regression loss are used to train our
DOR3D-Net in an end-to-end manner. Extensive experiments on public datasets
(ICVL, MSRA, NYU and HANDS2017) show that our design provides significant
improvements over SOTA methods.
