  Due to the few annotated labels of 3D point clouds, how to learn
discriminative features of point clouds to segment object instances is a
challenging problem. In this paper, we propose a simple yet effective 3D
instance segmentation framework that can achieve good performance by annotating
only one point for each instance. Specifically, to tackle extremely few labels
for instance segmentation, we first oversegment the point cloud into
superpoints in an unsupervised manner and extend the point-level annotations to
the superpoint level. Then, based on the superpoint graph, we propose an
inter-superpoint affinity mining module that considers the semantic and spatial
relations to adaptively learn inter-superpoint affinity to generate
high-quality pseudo labels via semantic-aware random walk. Finally, we propose
a volume-aware instance refinement module to segment high-quality instances by
applying volume constraints of objects in clustering on the superpoint graph.
Extensive experiments on the ScanNet-v2 and S3DIS datasets demonstrate that our
method achieves state-of-the-art performance in the weakly supervised point
cloud instance segmentation task, and even outperforms some fully supervised
methods.
