Semi-supervised 3D shape segmentation with multilevel consistency and part substitution

Computational Visual Media

Research Article

Yu-Qi Yang¹  Hao-Xiang Guo¹  Heung-Yeung Shum¹  Chun-Yu Sun¹  Xin Tong²  Peng-Shuai Wang²  Yang Liu² 

[1] Institute for Advanced Study, Tsinghua University, 100084, Beijing, China;Microsoft Research Asia, 100080, Beijing, China;

关键词: shape segmentation;    semi-supervised learning;    multilevel consistency;   

DOI  :  10.1007/s41095-022-0281-9

 received in 2022-01-06, accepted in 2022-03-05,  发布年份 2022

来源: Springer

【 摘 要 】

The lack of fine-grained 3D shape segmentation data is the main obstacle to developing learning-based 3D segmentation techniques. We propose an effective semi-supervised method for learning 3D segmentations from a few labeled 3D shapes and a large amount of unlabeled 3D data. For the unlabeled data, we present a novel multilevel consistency loss to enforce consistency of network predictions between perturbed copies of a 3D shape at multiple levels: point level, part level, and hierarchical level. For the labeled data, we develop a simple yet effective part substitution scheme to augment the labeled 3D shapes with more structural variations to enhance training. Our method has been extensively validated on the task of 3D object semantic segmentation on PartNet and ShapeNetPart, and indoor scene semantic segmentation on ScanNet. It exhibits superior performance to existing semi-supervised and unsupervised pre-training 3D approaches.

【 授权许可】

CC BY   
© The Author(s) 2022