No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation

Xiangyang Zhu, Renrui Zhang, Bowei He, Ziyu Guo, Jiaming Liu, Han Xiao, Chaoyou Fu, Hao Dong, Peng Gao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 3838-3847

To reduce the reliance on large-scale datasets recent works in 3D segmentation resort to few-shot learning. Current 3D few-shot segmentation methods first pre-train models on 'seen' classes and then evaluate their generalization performance on 'unseen' classes. However the prior pre-training stage not only introduces excessive time overhead but also incurs a significant domain gap on 'unseen' classes. To tackle these issues we propose a Non-parametric Network for few-shot 3D Segmentation Seg-NN and its Parametric variant Seg-PN. Without training Seg-NN extracts dense representations by hand-crafted filters and achieves comparable performance to existing parameterized models. Due to the elimination of pre-training Seg-NN can alleviate the domain gap issue and save a substantial amount of time. Based on Seg-NN Seg-PN only requires training a lightweight QUEry-Support Transferring (QUEST) module which enhances the interaction between the support set and query set. Experiments suggest that Seg-PN outperforms previous state-of-the-art method by +4.19% and +7.71% mIoU on S3DIS and ScanNet datasets respectively while reducing training time by -90% indicating its effectiveness and efficiency. Code is available https://github.com/yangyangyang127/Seg-NN.