  In human-computer interaction, it is important to accurately estimate the
hand pose especially fingertips. However, traditional approaches for fingertip
localization mainly rely on depth images and thus suffer considerably from the
noise and missing values. Instead of depth images, stereo images can also
provide 3D information of hands and promote 3D hand pose estimation. There are
nevertheless limitations on the dataset size, global viewpoints, hand
articulations and hand shapes in the publicly available stereo-based hand pose
datasets. To mitigate these limitations and promote further research on hand
pose estimation from stereo images, we propose a new large-scale binocular hand
pose dataset called THU-Bi-Hand, offering a new perspective for fingertip
localization. In the THU-Bi-Hand dataset, there are 447k pairs of stereo images
of different hand shapes from 10 subjects with accurate 3D location annotations
of the wrist and five fingertips. Captured with minimal restriction on the
range of hand motion, the dataset covers large global viewpoint space and hand
articulation space. To better present the performance of fingertip localization
on THU-Bi-Hand, we propose a novel scheme termed Bi-stream Pose Guided Region
Ensemble Network (Bi-Pose-REN). It extracts more representative feature regions
around joint points in the feature maps under the guidance of the previously
estimated pose. The feature regions are integrated hierarchically according to
the topology of hand joints to regress the refined hand pose. Bi-Pose-REN and
several existing methods are evaluated on THU-Bi-Hand so that benchmarks are
provided for further research. Experimental results show that our new method
has achieved the best performance on THU-Bi-Hand.
