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Learning on point cloud is eagerly in demand because the point cloud is acommon type of geometric data and can aid robots to understand environmentsrobustly. However, the point cloud is sparse, unstructured, and unordered,which cannot be recognized accurately by a traditional convolutional neuralnetwork (CNN) nor a recurrent neural network (RNN). Fortunately, a graphconvolutional neural network (Graph CNN) can process sparse and unordered data.Hence, we propose a linked dynamic graph CNN (LDGCNN) to classify and segmentpoint cloud directly in this paper. We remove the transformation network, linkhierarchical features from dynamic graphs, freeze feature extractor, andretrain the classifier to increase the performance of LDGCNN. We explain ournetwork using theoretical analysis and visualization. Through experiments, weshow that the proposed LDGCNN achieves state-of-art performance on two standarddatasets: ModelNet40 and ShapeNet.

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