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.

通过提出神经内在嵌入（NIE）来嵌入每个顶点到高维空间从而建立弱监督的非刚性点云配准框架，无需先前的基础构建或真实配准标签。实验证明我们的框架表现与现代方法相当甚至更好，而这些方法通常需要更多的监督和 / 或结构几何输入。

非刚性点云匹配的神经内在嵌入