Abstract
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.