  We tackle the novel class discovery in point cloud segmentation, which
discovers novel classes based on the semantic knowledge of seen classes.
Existing work proposes an online point-wise clustering method with a simplified
equal class-size constraint on the novel classes to avoid degenerate solutions.
However, the inherent imbalanced distribution of novel classes in point clouds
typically violates the equal class-size constraint. Moreover, point-wise
clustering ignores the rich spatial context information of objects, which
results in less expressive representation for semantic segmentation. To address
the above challenges, we propose a novel self-labeling strategy that adaptively
generates high-quality pseudo-labels for imbalanced classes during model
training. In addition, we develop a dual-level representation that incorporates
regional consistency into the point-level classifier learning, reducing noise
in generated segmentation. Finally, we conduct extensive experiments on two
widely used datasets, SemanticKITTI and SemanticPOSS, and the results show our
method outperforms the state of the art by a large margin.
