We consider representation learning from 3D graphs in which each node is associated with a spatial position in 3D. This is an under explored area of research, and a principled framework is currently lacking. In this work, we propose a generic framework, known as the 3D graph network (3DGN), to provide a unified interface at different levels of granularity for 3D graphs. Built on 3DGN, we propose the spherical message passing (SMP) as a novel and specific scheme for realizing the 3DGN framework in the spherical coordinate system (SCS). We conduct formal analyses and show that the relative location of each node in 3D graphs is uniquely defined in the SMP scheme. Thus, our SMP represents a complete and accurate architecture for learning from 3D graphs in the SCS. We derive physically-based representations of geometric information and propose the SphereNet for learning representations of 3D graphs. We show that existing 3D deep models can be viewed as special cases of the SphereNet. Experimental results demonstrate that the use of complete and accurate 3D information in 3DGN and SphereNet leads to significant performance improvements in prediction tasks.
翻译:我们考虑从3D图解中学习3D图解,其中每个节点与3D空间位置相关。这是一个探索研究领域,目前缺乏一个原则框架。在这项工作中,我们提议了一个通用框架,称为3D图解网络(3DGN),为3D图解的不同颗粒层提供一个统一的界面。我们建在3DGN,我们提议球形信息传递(SMP),作为在球形协调系统中实现3DGN框架的一个新而具体的计划。我们进行正式分析,并表明3D图中每个节点的相对位置在SMP计划中是独特的定义。因此,我们的SMP是一个从3D图解剖中学习的完整和准确的结构。我们从物理上对地理学信息进行表述,并提议SphereNet用于学习3D图解析。我们表明现有的3D深线模型可以被视为SphereNet的特殊案例。实验结果显示,3DGN和SphereNet中完整和准确的3D信息的使用在3DGN和SpheNet中可以导致显著的预测。