  3D scene understanding, e.g., point cloud semantic and instance segmentation,
often requires large-scale 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 the
per-category prototype to enhance the pseudo label quality and guide the
iterative training. Besides, our model can be compatible to 3D instance
segmentation equipped with a point-clustering strategy. 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 and instance segmentation by a large margin, and our
results are also comparable to those of the fully supervised counterparts.
Codes and models are available at
https://github.com/liuzhengzhe/One-Thing-One-Click.
