Unsupervised Feature Learning for Point Cloud Understanding by Contrasting and Clustering Using Graph Convolutional Neural Networks

To alleviate the cost of collecting and annotating large-scale "3D object" point cloud data, we propose an unsupervised learning approach to learn features from an unlabeled point cloud dataset by using part contrasting and object clustering with deep graph convolutional neural networks (GCNNs). In the contrast learning step, all the samples in the 3D object dataset are cut into two parts and put into a "part" dataset. Then a contrast learning GCNN (ContrastNet) is trained to verify whether two randomly sampled parts from the part dataset belong to the same object.