Generalized Few-Shot 3D Segmentation and More: Support-Free Novel-Class Discovery

Existing generalized few-shot 3D semantic segmentation methods typically assume a fixed set of novel classes and rely on noisy pointwise pseudo-labels and limited support prototypes, making missed novel-class instances difficult to recover in both spatial structure and semantics. To address this issue, we focus on novel-instance recovery directly from the query scene and propose Open-World Posterior Completion (OW-PC), a nonparametric framework built upon distance-dependent Chinese Restaurant Process (ddCRP) for structure-aware novel-class discovery. Specifically, Dual-View Debiasing (DVD) integrates complementary pretrained semantic knowledge and episodic support to extract reliable novel-class evidence, while Counterfactual Base Guard (CBG) preserves confident base-class predictions and exposes ambiguous regions for further exploration. OW-PC then organizes sparse novel evidence into coherent instance structures through spatial and semantic consistency, enabling missed novel instances to be recovered both with and without available support. Unlike conventional support-conditioned refinement, its nonparametric formulation does not require the number of novel classes to be fixed in advance, allowing potential novel structures to be actively discovered from the query scene. Experiments on ScanNet, ScanNet++, and ScanNet200 demonstrate consistent improvements in novel-class recall and harmonic mean while preserving base-class segmentation accuracy.