  Addressing the annotation challenge in 3D Point Cloud segmentation has
inspired research into weakly supervised learning. Existing approaches mainly
focus on exploiting manifold and pseudo-labeling to make use of large unlabeled
data points. A fundamental challenge here lies in the large intra-class
variations of local geometric structure, resulting in subclasses within a
semantic class. In this work, we leverage this intuition and opt for
maintaining an individual classifier for each subclass. Technically, we design
a multi-prototype classifier, each prototype serves as the classifier weights
for one subclass. To enable effective updating of multi-prototype classifier
weights, we propose two constraints respectively for updating the prototypes
w.r.t. all point features and for encouraging the learning of diverse
prototypes. Experiments on weakly supervised 3D point cloud segmentation tasks
validate the efficacy of proposed method in particular at low-label regime. Our
hypothesis is also verified given the consistent discovery of semantic
subclasses at no cost of additional annotations.
