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.

为了减少对大规模数据集的依赖，最近在 3D 分割方面的研究转向了少样本学习。本文提出了一种高效的无训练的 3D 少样本分割网络 TFS3D 和基于训练的变种 TFS3D-T，通过三角函数位置编码提取密集表示，取得了可与以往基于训练方法相媲美的性能。与预训练方法相比，TFS3D 能够缓解领域差异问题并节省大量时间。基于 TFS3D，TFS3D-T 只需训练一个轻量级的查询 - 支持转移注意力，增强了少样本查询和支持数据之间的交互。实验结果表明，在 S3DIS 和 ScanNet 上，TFS3D-T 分别将最先进方法的 mIoU 提高了 6.93% 和 17.96%，同时训练时间减少了 90%，显示出卓越的有效性和高效性。