Point cloud analysis is very challenging, as the shape implied in irregular
points is difficult to capture. In this paper, we propose RS-CNN, namely,
Relation-Shape Convolutional Neural Network, which extends regular grid CNN to
irregular configuration for point cloud analysis. The key to RS-CNN is learning
from relation, i.e., the geometric topology constraint among points.
Specifically, the convolutional weight for local point set is forced to learn a
high-level relation expression from predefined geometric priors, between a
sampled point from this point set and the others. In this way, an inductive
local representation with explicit reasoning about the spatial layout of points
can be obtained, which leads to much shape awareness and robustness. With this
convolution as a basic operator, RS-CNN, a hierarchical architecture can be
developed to achieve contextual shape-aware learning for point cloud analysis.
Extensive experiments on challenging benchmarks across three tasks verify
RS-CNN achieves the state of the arts.

该研究提出了 RS-CNN 方法，即关系形状卷积神经网络，将规则网格 CNN 扩展为点云分析的不规则配置。RS-CNN 的关键是通过学习关系来实现点之间的几何拓扑约束，从而获得具有形状意识和鲁棒性的点云局部表达，实现了针对点云分析的上下文形状感知学习。实验结果表明，RS-CNN 达到了业界领先水平。