  In this paper, we propose a transformer-based procedure for the efficient
registration of non-rigid 3D point clouds. The proposed approach is data-driven
and adopts for the first time the transformer architecture in the registration
task. Our method is general and applies to different settings. Given a fixed
template with some desired properties (e.g. skinning weights or other animation
cues), we can register raw acquired data to it, thereby transferring all the
template properties to the input geometry. Alternatively, given a pair of
shapes, our method can register the first onto the second (or vice-versa),
obtaining a high-quality dense correspondence between the two. In both
contexts, the quality of our results enables us to target real applications
such as texture transfer and shape interpolation. Furthermore, we also show
that including an estimation of the underlying density of the surface eases the
learning process. By exploiting the potential of this architecture, we can
train our model requiring only a sparse set of ground truth correspondences
($10\sim20\%$ of the total points). The proposed model and the analysis that we
perform pave the way for future exploration of transformer-based architectures
for registration and matching applications. Qualitative and quantitative
evaluations demonstrate that our pipeline outperforms state-of-the-art methods
for deformable and unordered 3D data registration on different datasets and
scenarios.
