  Existing state-of-the-art 3D point clouds understanding methods only perform
well in a fully supervised manner. To the best of our knowledge, there exists
no unified framework which simultaneously solves the downstream high-level
understanding tasks, especially when labels are extremely limited. This work
presents a general and simple framework to tackle point clouds understanding
when labels are limited. We propose a novel unsupervised region expansion based
clustering method for generating clusters. More importantly, we innovatively
propose to learn to merge the over-divided clusters based on the local
low-level geometric property similarities and the learned high-level feature
similarities supervised by weak labels. Hence, the true weak labels guide
pseudo labels merging taking both geometric and semantic feature correlations
into consideration. Finally, the self-supervised reconstruction and data
augmentation optimization modules are proposed to guide the propagation of
labels among semantically similar points within a scene. Experimental Results
demonstrate that our framework has the best performance among the three most
important weakly supervised point clouds understanding tasks including semantic
segmentation, instance segmentation, and object detection even when limited
points are labeled, under the data-efficient settings for the large-scale 3D
semantic scene parsing. The developed techniques have postentials to be applied
to downstream tasks for better representations in robotic manipulation and
robotic autonomous navigation. Codes and models are publicly available at:
https://github.com/KangchengLiu.
