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.

翻译：现有最先进的3D点云理解方法仅在完全监督下表现良好。据我们所知，尚不存在能同时解决下游高级理解任务的统一框架，尤其是在标签极其有限的情况下。本文提出一个通用且简洁的框架，用于应对标签有限条件下的点云理解问题。我们提出一种基于无监督区域扩展的聚类方法以生成聚类簇。更重要的是，我们创新性地提出基于局部低层几何属性相似性以及由弱标签监督所学的高层特征相似性，学习合并过度分割的聚类簇。因此，真实弱标签通过同时考虑几何与语义特征相关性来指导伪标签合并。最后，引入自监督重建与数据增强优化模块，以引导场景内语义相似点间的标签传播。实验结果表明，在大规模3D语义场景解析的数据高效设置下，即便仅标注少量点，我们的框架在语义分割、实例分割和物体检测这三项最重要的弱监督点云理解任务中均达到最优性能。所开发的技术有望应用于下游任务，以提升机器人操作与自主导航中的表征能力。代码与模型已在https://github.com/KangchengLiu 公开。