Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided Enhancement
This work addresses the problem of few-shot segmentation for 3D point clouds, which is important for applications like robotics and autonomous driving, but it is incremental as it builds on existing prototype-based methods.
The paper tackles the challenge of adapting 3D point cloud segmentation models to novel categories in few-shot settings by proposing a query-guided enhancement method that reduces contextual gaps between support prototypes and query features, achieving significant improvements on S3DIS and ScanNet datasets.
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