Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features

Understand

Learning on point cloud is eagerly in demand because the point cloud is a common type of geometric data and can aid robots to understand environments robustly.

• However, the point cloud is sparse, unstructured, and unordered, which cannot be recognized accurately by a traditional convolutional neural network (CNN) nor a recurrent neural network (RNN).
• Fortunately, a graph convolutional neural network (Graph CNN) can process sparse and unordered data.
• Hence, we propose a linked dynamic graph CNN (LDGCNN) to classify and segment point cloud directly in this paper.