  Semi-supervised learning (SSL), thanks to the significant reduction of data
annotation costs, has been an active research topic for large-scale 3D scene
understanding. However, the existing SSL-based methods suffer from severe
training bias, mainly due to class imbalance and long-tail distributions of the
point cloud data. As a result, they lead to a biased prediction for the tail
class segmentation. In this paper, we introduce a new decoupling optimization
framework, which disentangles feature representation learning and classifier in
an alternative optimization manner to shift the bias decision boundary
effectively. In particular, we first employ two-round pseudo-label generation
to select unlabeled points across head-to-tail classes. We further introduce
multi-class imbalanced focus loss to adaptively pay more attention to feature
learning across head-to-tail classes. We fix the backbone parameters after
feature learning and retrain the classifier using ground-truth points to update
its parameters. Extensive experiments demonstrate the effectiveness of our
method outperforming previous state-of-the-art methods on both indoor and
outdoor 3D point cloud datasets (i.e., S3DIS, ScanNet-V2, Semantic3D, and
SemanticKITTI) using 1% and 1pt evaluation.
