  Labelling point clouds fully is highly time-consuming and costly. As larger
point cloud datasets with billions of points become more common, we ask whether
the full annotation is even necessary, demonstrating that existing baselines
designed under a fully annotated assumption only degrade slightly even when
faced with 1% random point annotations. However, beyond this point, e.g., at
0.1% annotations, segmentation accuracy is unacceptably low. We observe that,
as point clouds are samples of the 3D world, the distribution of points in a
local neighborhood is relatively homogeneous, exhibiting strong semantic
similarity. Motivated by this, we propose a new weak supervision method to
implicitly augment highly sparse supervision signals. Extensive experiments
demonstrate the proposed Semantic Query Network (SQN) achieves promising
performance on seven large-scale open datasets under weak supervision schemes,
while requiring only 0.1% randomly annotated points for training, greatly
reducing annotation cost and effort. The code is available at
https://github.com/QingyongHu/SQN.
