CascadeProto: Cascaded Cross-Modal Prototype Purification via Entropy-Aware Learning for Few-Shot 3D Point Cloud Segmentation

Few-shot 3D point cloud semantic segmentation aims to rec-ognize novel object categories with limited labeled examples. Existingmethods typically rely on point-based prototypes extracted from sup-port sets, but such prototypes inevitably contain background noise andlack semantic richness, leading to suboptimal segmentation quality. In-spired by chemical distillation that iteratively removes impurities, wepropose CascadeProto, which employs multi-step refinement to distillhigh-quality prototypes from noisy initializations. At its core lies theEntropy-aware Prototype Purification Module (EPPM), which lever-ages information-theoretic principles to suppress high-entropy backgroundfeatures while amplifying low-entropy foreground information. To fur-ther enrich prototype representations, we introduce Learnable ModalityAdapters (LMA) that independently align each of three CLIP modal-ities — text, audio, and image — with point cloud features throughforeground-background decoupled distribution matching, enabling flexi-ble single-modality semantic enrichment that bridges the 2D-3D domaingap. Furthermore, we propose an Attention-based Dynamic RoutingMechanism (ADRM) that adaptively aggregates predictions from mul-tiple cascade stages, allowing simple regions to benefit from early-stageoutputs while complex regions leverage deeper purification. Extensiveexperiments on S3DIS and ScanNet benchmarks demonstrate the effec-tiveness and superiority of CascadeProto across all three modalities overstate-of-the-art methods. The code is available at https://github.com/changshuowang/CascadeProto.