  We study the problem of labelling effort for semantic segmentation of
large-scale 3D point clouds. Existing works usually rely on densely annotated
point-level semantic labels to provide supervision for network training.
However, in real-world scenarios that contain billions of points, it is
impractical and extremely costly to manually annotate every single point. In
this paper, we first investigate whether dense 3D labels are truly required for
learning meaningful semantic representations. Interestingly, we find that the
segmentation performance of existing works only drops slightly given as few as
1% of the annotations. However, beyond this point (e.g. 1 per thousand and
below) existing techniques fail catastrophically. To this end, we propose a new
weak supervision method to implicitly augment the total amount of available
supervision signals, by leveraging the semantic similarity between neighboring
points. Extensive experiments demonstrate that the proposed Semantic Query
Network (SQN) achieves state-of-the-art performance on six large-scale open
datasets under weak supervision schemes, while requiring only 1000x fewer
labeled points for training. The code is available at
https://github.com/QingyongHu/SQN.
