  To reduce the reliance on large-scale datasets, recent works in 3D
segmentation resort to few-shot learning. Current 3D few-shot semantic
segmentation methods first pre-train the 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 an efficient Training-free Few-shot 3D Segmentation netwrok, TFS3D, and
a further training-based variant, TFS3D-T. Without any learnable parameters,
TFS3D extracts dense representations by trigonometric positional encodings, and
achieves comparable performance to previous training-based methods. Due to the
elimination of pre-training, TFS3D can alleviate the domain gap issue and save
a substantial amount of time. Building upon TFS3D, TFS3D-T only requires to
train a lightweight query-support transferring attention (QUEST), which
enhances the interaction between the few-shot query and support data.
Experiments demonstrate TFS3D-T improves previous state-of-the-art methods by
+6.93% and +17.96% mIoU respectively on S3DIS and ScanNet, while reducing the
training time by -90%, indicating superior effectiveness and efficiency.

Computer Vision and Pattern Recognition