  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.
