  As a primitive 3D data representation, point clouds are prevailing in 3D
sensing, yet short of intrinsic structural information of the underlying
objects. Such discrepancy poses great challenges on directly establishing
correspondences between point clouds sampled from deformable shapes. In light
of this, we propose Neural Intrinsic Embedding (NIE) to embed each vertex into
a high-dimensional space in a way that respects the intrinsic structure. Based
upon NIE, we further present a weakly-supervised learning framework for
non-rigid point cloud registration. Unlike the prior works, we do not require
expansive and sensitive off-line basis construction (e.g., eigen-decomposition
of Laplacians), nor do we require ground-truth correspondence labels for
supervision. We empirically show that our framework performs on par with or
even better than the state-of-the-art baselines, which generally require more
supervision and/or more structural geometric input.
