LDSGSeg: Local Distribution Encoding and Semantic Guidance for Few-Shot Point Cloud Semantic Segmentation

Few-shot 3D point cloud semantic segmentation aims to segment novel classes in query point clouds given only a few annotated support samples. However, existing prototype-based methods still encounter two limitations: i) they do not explicitly characterize the spatial distribution of neighboring points; ii) query–prototype matching relies mainly on visual representations, without fully exploiting the category-level semantic guidance provided by class names. To address these issues, we propose LDSGSeg, a novel framework that integrates local distribution encoding with category-level semantic guidance. Specifically, to solve the first issue, PCA-Guided Local Distribution Encoding derives local distribution descriptors from local covariance eigenvalues to enrich local point representations. To address the second issue, Point-to-Text Cross-Attention introduces category-level semantic information into query point features by modeling their relationships with the text representations of foreground classes. Furthermore, Text-Guided Prototype Channel Calibration employs the corresponding class text embedding to adaptively reweight the channels of each support-derived visual prototype, thereby providing category-level semantic guidance for prototype matching. Extensive experiments on the S3DIS and ScanNet datasets demonstrate that LDSGSeg consistently improves segmentation performance under various few-shot settings.