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

• URL: http://arxiv.org/abs/2303.14727v2
• Date: Sat, 9 Sep 2023 23:11:09 GMT
• Title: You Only Need One Thing One Click: Self-Training for Weakly Supervised 3D Scene Understanding
• Authors: Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu
• Abstract summary: We propose One Thing One Click'', meaning that the annotator only needs to label one point per object. We iteratively conduct the training and label propagation, facilitated by a graph propagation module. Our model can be compatible to 3D instance segmentation equipped with a point-clustering strategy.
• Score: 107.06117227661204
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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