  Point cloud segmentation is a fundamental visual understanding task in 3D
vision. A fully supervised point cloud segmentation network often requires a
large amount of data with point-wise annotations, which is expensive to obtain.
In this work, we present the Compositional Prototype Network that can undertake
point cloud segmentation with only a few labeled training data. Inspired by the
few-shot learning literature in images, our network directly transfers label
information from the limited training data to unlabeled test data for
prediction. The network decomposes the representations of complex point cloud
data into a set of local regional representations and utilizes them to
calculate the compositional prototypes of a visual concept. Our network
includes a key Multi-View Comparison Component that exploits the redundant
views of the support set. To evaluate the proposed method, we create a new
segmentation benchmark dataset, ScanNet-$6^i$, which is built upon ScanNet
dataset. Extensive experiments show that our method outperforms baselines with
a significant advantage. Moreover, when we use our network to handle the
long-tail problem in a fully supervised point cloud segmentation dataset, it
can also effectively boost the performance of the few-shot classes.
