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.

翻译：作为一种基础的3D数据表示方式，点云在三维感知中广泛应用，但缺乏底层物体的内在结构信息。这种差异为从可变形形状采样的点云之间直接建立对应关系带来了巨大挑战。为此，我们提出神经内在嵌入（Neural Intrinsic Embedding，NIE），将每个顶点嵌入到高维空间，同时保持内在结构。基于NIE，我们进一步提出一种弱监督学习框架，用于非刚性点云配准。与先前工作不同，我们不需要昂贵且敏感的离线基构造（例如拉普拉斯算子的特征分解），也不需要真实对应标签进行监督。实验表明，我们的框架性能与最先进的基线方法相当甚至更优，而这些基线方法通常需要更多的监督信号和/或更多的结构几何输入。