  To alleviate the cost of collecting and annotating large-scale point cloud
datasets, we propose an unsupervised learning approach to learn features from
unlabeled point cloud "3D object" dataset by using part contrasting and object
clustering with deep graph neural networks (GNNs). 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 GNN (ContrastNet) is trained to
verify whether two randomly sampled parts from the part dataset belong to the
same object. In the cluster learning step, the trained ContrastNet is applied
to all the samples in the original 3D object dataset to extract features, which
are used to group the samples into clusters. Then another GNN for clustering
learning (ClusterNet) is trained to predict the cluster ID of all the training
samples. The contrasting learning forces the ContrastNet to learn high-level
semantic features of objects but probably ignores low-level features, while the
ClusterNet improves the quality of learned features by being trained to
discover objects that probably belong to the same semantic categories by the
use of cluster IDs. We have conducted extensive experiments to evaluate the
proposed framework on point cloud classification tasks. The proposed
unsupervised learning approach obtained comparable performance to the
state-of-the-art unsupervised learning methods that used much more complicated
network structures. The code of this work is publicly available via:
https://github.com/lingzhang1/ContrastNet.
