Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention
Jiahui Wang,Haiyue Zhu,Haoren Guo,Abdullah Al Mamun,Cheng Xiang,Tong Heng Lee,Jiahui Wang,Haiyue Zhu,Haoren Guo,Abdullah Al Mamun,Cheng Xiang,Tong Heng Lee
This paper presents an effective few-shot point cloud semantic segmentation approach for real-world applications. Existing few-shot segmentation methods on point cloud heavily rely on the fully-supervised pretrain with large annotated datasets, which causes the learned feature extraction bias to those pretrained classes. However, as the purpose of few-shot learning is to handle unknown/unseen clas...
