  Weakly supervised point cloud segmentation, i.e. semantically segmenting a
point cloud with only a few labeled points in the whole 3D scene, is highly
desirable due to the heavy burden of collecting abundant dense annotations for
the model training. However, existing methods remain challenging to accurately
segment 3D point clouds since limited annotated data may lead to insufficient
guidance for label propagation to unlabeled data. Considering the
smoothness-based methods have achieved promising progress, in this paper, we
advocate applying the consistency constraint under various perturbations to
effectively regularize unlabeled 3D points. Specifically, we propose a novel
DAT (\textbf{D}ual \textbf{A}daptive \textbf{T}ransformations) model for weakly
supervised point cloud segmentation, where the dual adaptive transformations
are performed via an adversarial strategy at both point-level and region-level,
aiming at enforcing the local and structural smoothness constraints on 3D point
clouds. We evaluate our proposed DAT model with two popular backbones on the
large-scale S3DIS and ScanNet-V2 datasets. Extensive experiments demonstrate
that our model can effectively leverage the unlabeled 3D points and achieve
significant performance gains on both datasets, setting new state-of-the-art
performance for weakly supervised point cloud segmentation.
