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

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arXiv · 2404.04050

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

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Abstract

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 parametric 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.

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BibTeX RIS

Xiangyang Zhu, Renrui Zhang, Bowei He, Ziyu Guo, Jiaming Liu, Han Xiao, Chaoyou Fu, Hao Dong, Peng Gao. 2024-04-05. No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation. https://arxiv.org/abs/2404.04050

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