  Existing state-of-the-art 3D point cloud understanding methods merely perform
well in a fully supervised manner. To the best of our knowledge, there exists
no unified framework that simultaneously solves the downstream high-level
understanding tasks including both segmentation and detection, especially when
labels are extremely limited. This work presents a general and simple framework
to tackle point cloud understanding when labels are limited. The first
contribution is that we have done extensive methodology comparisons of
traditional and learned 3D descriptors for the task of weakly supervised 3D
scene understanding, and validated that our adapted traditional PFH-based 3D
descriptors show excellent generalization ability across different domains. The
second contribution is that we proposed a learning-based region merging
strategy based on the affinity provided by both the traditional/learned 3D
descriptors and learned semantics. The merging process takes both low-level
geometric and high-level semantic feature correlations into consideration.
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 very limited number of points are labeled. Our method,
termed Region Merging 3D (RM3D), has superior performance on ScanNet
data-efficient learning online benchmarks and other four large-scale 3D
understanding benchmarks under various experimental settings, outperforming
current arts by a margin for various 3D understanding tasks without complicated
learning strategies such as active learning.
