  Instance segmentation on 3D point clouds has been attracting increasing
attention due to its wide applications, especially in scene understanding
areas. However, most existing methods operate on fully annotated data while
manually preparing ground-truth labels at point-level is very cumbersome and
labor-intensive. To address this issue, we propose a novel weakly supervised
method RWSeg that only requires labeling one object with one point. With these
sparse weak labels, we introduce a unified framework with two branches to
propagate semantic and instance information respectively to unknown regions
using self-attention and a cross-graph random walk method. Specifically, we
propose a Cross-graph Competing Random Walks (CRW) algorithm that encourages
competition among different instance graphs to resolve ambiguities in closely
placed objects, improving instance assignment accuracy. RWSeg generates
high-quality instance-level pseudo labels. Experimental results on ScanNet-v2
and S3DIS datasets show that our approach achieves comparable performance with
fully-supervised methods and outperforms previous weakly-supervised methods by
a substantial margin.
