  Point cloud semantic segmentation often requires largescale annotated
training data, but clearly, point-wise labels are too tedious to prepare. While
some recent methods propose to train a 3D network with small percentages of
point labels, we take the approach to an extreme and propose "One Thing One
Click," meaning that the annotator only needs to label one point per object. To
leverage these extremely sparse labels in network training, we design a novel
self-training approach, in which we iteratively conduct the training and label
propagation, facilitated by a graph propagation module. Also, we adopt a
relation network to generate per-category prototype and explicitly model the
similarity among graph nodes to generate pseudo labels to guide the iterative
training. Experimental results on both ScanNet-v2 and S3DIS show that our
self-training approach, with extremely-sparse annotations, outperforms all
existing weakly supervised methods for 3D semantic segmentation by a large
margin, and our results are also comparable to those of the fully supervised
counterparts.
