One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation

Zhengzhe LIU, Xiaojuan QI*, Chi-Wing FU

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

118 Citations (Scopus)

Abstract

Point cloud semantic 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 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.
Original languageEnglish
Title of host publicationProceedings : 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
PublisherIEEE Computer Society
Pages1726-1736
Number of pages11
ISBN (Electronic)9781665445092
DOIs
Publication statusPublished - 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 IEEE

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