  Point cloud is a principal data structure adopted for 3D geometric
information encoding. Unlike other conventional visual data, such as images and
videos, these irregular points describe the complex shape features of 3D
objects, which makes shape feature learning an essential component of point
cloud analysis. To this end, a shape-oriented message passing scheme dubbed
ShapeConv is proposed to focus on the representation learning of the underlying
shape formed by each local neighboring point. Despite this intra-shape
relationship learning, ShapeConv is also designed to incorporate the contextual
effects from the inter-shape relationship through capturing the long-ranged
dependencies between local underlying shapes. This shape-oriented operator is
stacked into our hierarchical learning architecture, namely Shape-Oriented
Convolutional Neural Network (SOCNN), developed for point cloud analysis.
Extensive experiments have been performed to evaluate its significance in the
tasks of point cloud classification and part segmentation.
