  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
https://github.com/AaronNZH/Boosting-Few-shot-3D-Point-Cloud-Segmentation-via-Query-Guided-Enhancement
