Although extensive research has been conducted on 3D point cloud
segmentation, effectively adapting generic models to novel categories remains a
formidable challenge. This paper proposes a novel approach to improve point
cloud few-shot segmentation (PC-FSS) models. Unlike existing PC-FSS methods
that directly utilize categorical information from support prototypes to
recognize novel classes in query samples, our method identifies two critical
aspects that substantially enhance model performance by reducing contextual
gaps between support prototypes and query features. Specifically, we (1) adapt
support background prototypes to match query context while removing extraneous
cues that may obscure foreground and background in query samples, and (2)
holistically rectify support prototypes under the guidance of query features to
emulate the latter having no semantic gap to the query targets. Our proposed
designs are agnostic to the feature extractor, rendering them readily
applicable to any prototype-based methods. The experimental results on S3DIS
and ScanNet demonstrate notable practical benefits, as our approach achieves
significant improvements while still maintaining high efficiency. The code for
our approach is available at
this https URL

通过适应支持原型和查询特征之间的语义差距，我们的方法在点云的少样本分割模型中取得显著改进，并维持高效性能。

通过查询引导增强提升少样本 3D 点云分割

Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided  Enhancement

In this paper we reformulate few-shot classification as a reconstruction
problem in latent space. The ability of the network to reconstruct a query
feature map from support features of a given class predicts membership of the
query in that class. We introduce a novel mechanism for few-shot classification
by regressing directly from support features to query features in closed form,
without introducing any new modules or large-scale learnable parameters. The
resulting Feature Map Reconstruction Networks are both more performant and
computationally efficient than previous approaches. We demonstrate consistent
and substantial accuracy gains on four fine-grained benchmarks with varying
neural architectures. Our model is also competitive on the non-fine-grained
mini-ImageNet and tiered-ImageNet benchmarks with minimal bells and whistles.

本文提出了一种在潜在空间中将 few-shot 分类问题重新表述为重建问题的方法。通过从给定类别的支持特征到查询特征的直接回归，无需引入新的模块或大规模可学习参数，我们引入了一种新的机制进行 Few-shot 分类，该机制被称为特征映射重建网络，它比以前的方法更有效和更高效。我们在四种细粒度基准测试和两种非细粒度基准测试上验证了这个方法的一致和显著的准确性增益。