  3D point cloud semantic segmentation is one of the fundamental tasks for 3D
scene understanding and has been widely used in the metaverse applications.
Many recent 3D semantic segmentation methods learn a single prototype
(classifier weights) for each semantic class, and classify 3D points according
to their nearest prototype. However, learning only one prototype for each class
limits the model's ability to describe the high variance patterns within a
class. Instead of learning a single prototype for each class, in this paper, we
propose to use an adaptive number of prototypes to dynamically describe the
different point patterns within a semantic class. With the powerful capability
of vision transformer, we design a Number-Adaptive Prototype Learning (NAPL)
model for point cloud semantic segmentation. To train our NAPL model, we
propose a simple yet effective prototype dropout training strategy, which
enables our model to adaptively produce prototypes for each class. The
experimental results on SemanticKITTI dataset demonstrate that our method
achieves 2.3% mIoU improvement over the baseline model based on the point-wise
classification paradigm.
