  Since the preparation of labeled data for training semantic segmentation
networks of point clouds is a time-consuming process, weakly supervised
approaches have been introduced to learn from only a small fraction of data.
These methods are typically based on learning with contrastive losses while
automatically deriving per-point pseudo-labels from a sparse set of
user-annotated labels. In this paper, our key observation is that the selection
of what samples to annotate is as important as how these samples are used for
training. Thus, we introduce a method for weakly supervised segmentation of 3D
scenes that combines self-training with active learning. The active learning
selects points for annotation that likely result in performance improvements to
the trained model, while the self-training makes efficient use of the
user-provided labels for learning the model. We demonstrate that our approach
leads to an effective method that provides improvements in scene segmentation
over previous works and baselines, while requiring only a small number of user
annotations.
