  Estimating 3D hand poses from a single RGB image is challenging because depth
ambiguity leads the problem ill-posed. Training hand pose estimators with 3D
hand mesh annotations and multi-view images often results in significant
performance gains. However, existing multi-view datasets are relatively small
with hand joints annotated by off-the-shelf trackers or automated through model
predictions, both of which may be inaccurate and can introduce biases.
Collecting a large-scale multi-view 3D hand pose images with accurate mesh and
joint annotations is valuable but strenuous. In this paper, we design a spin
match algorithm that enables a rigid mesh model matching with any target mesh
ground truth. Based on the match algorithm, we propose an efficient pipeline to
generate a large-scale multi-view hand mesh (MVHM) dataset with accurate 3D
hand mesh and joint labels. We further present a multi-view hand pose
estimation approach to verify that training a hand pose estimator with our
generated dataset greatly enhances the performance. Experimental results show
that our approach achieves the performance of 0.990 in
$\text{AUC}_{\text{20-50}}$ on the MHP dataset compared to the previous
state-of-the-art of 0.939 on this dataset. Our datasset is public available.
\footnote{\url{https://github.com/Kuzphi/MVHM}} Our datasset is available
at~\href{https://github.com/Kuzphi/MVHM}{\color{blue}{https://github.com/Kuzphi/MVHM}}.
