A Dynamic Reduction Network for Point Clouds

Understand

Classifying whole images is a classic problem in machine learning, and graph neural networks are a powerful methodology to learn highly irregular geometries.

• It is often the case that certain parts of a point cloud are more important than others when determining overall classification.
• On graph structures this started by pooling information at the end of convolutional filters, and has evolved to a variety of staged pooling techniques on static graphs.
• In this paper, a dynamic graph formulation of pooling is introduced that removes the need for predetermined graph structure.