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3D scene understanding, e.g., point cloud semantic and instance segmentation,often requires large-scale annotated training data, but clearly, point-wiselabels are too tedious to prepare. While some recent methods propose to train a3D network with small percentages of point labels, we take the approach to anextreme and propose ``One Thing One Click,'' meaning that the annotator onlyneeds to label one point per object. To leverage these extremely sparse labelsin network training, we design a novel self-training approach, in which weiteratively conduct the training and label propagation, facilitated by a graphpropagation module. Also, we adopt a relation network to generate theper-category prototype to enhance the pseudo label quality and guide theiterative training. Besides, our model can be compatible to 3D instancesegmentation equipped with a point-clustering strategy. Experimental results onboth ScanNet-v2 and S3DIS show that our self-training approach, withextremely-sparse annotations, outperforms all existing weakly supervisedmethods for 3D semantic and instance segmentation by a large margin, and ourresults are also comparable to those of the fully supervised counterparts.Codes and models are available athttps://github.com/liuzhengzhe/One-Thing-One-Click.

You Only Need One Thing One Click: Self-Training for Weakly Supervised 3D Scene Understanding