不训练、无标注，仅靠多视角一致性实现3D点云新类分割
Training-Free Open-Vocabulary 3D Point-Cloud Segmentation on the Generalized Few-Shot Benchmark
- 用冻结的视觉语言模型和可提示分割器，通过多视角一致性判断新类
- 在ScanNet200上新类mIoU提升2.6，基类准确率损失小于0.5
- 无需支持样本，且注入支持反而降低性能，适合资源受限场景
通用少样本3D点云分割（GFS-PCS）要求模型将场景分为训练中见过的基类和一组新类。现有方法依赖密集但嘈杂的3D视觉-语言先验与少样本支持进行调和，但需基类3D标签、每轮训练及支持标注。本文探索完全无需这些条件：零训练、无3D标签、无支持样本。方法采用冻结的3D视觉-语言模型（RegionPLC）作为密集先验，搭配冻结的可提示概念分割器（SAM3），仅以新类名称为提示，并从带姿态的RGB视图中提取信息，通过跨视角一致性判定：一个点仅在多个视图均识别为新类时才被归为新类。在ScanNet200 GFS-PCS基准上，该全训练自由方案使新类mIoU提升+2.6，基类准确率下降不足0.5，恢复了训练有监督方法33%的新类性能差距。进一步表明，将支持样本引入为融合门或原型分类器，不仅无增益，反而因分类器导致性能下降。在更难的ScanNet++基准上，相同方法使新类mIoU提升+15.7（16.2→31.9），基类代价为1.7，调和均值从21.5升至31.1。
原文摘要 · Abstract (English)
Generalized few-shot 3D point-cloud segmentation (GFS-PCS) asks a model to segment a scene into many base classes seen at training time and a set of novel classes. The state of the art reaches novel classes by reconciling a dense but noisy 3D vision-language prior with the few-shot support, but it pays for this with base 3D labels, per-episode training, and the support annotations themselves. We ask how far the same reconciliation can go with none of these: no training, no 3D labels, and not even the few-shot support. We pair a frozen 3D vision-language model (RegionPLC) as a dense prior with a frozen promptable concept segmenter (SAM3), prompted by the bare novel class names and lifted from posed RGB views, and reconcile the two by cross-view consistency: a point becomes novel only when enough of the views that see it agree. On the ScanNet200 GFS-PCS benchmark this fully training-free, open-vocabulary pipeline improves novel mIoU by +2.6 over the training-free dense prior while holding base accuracy within 0.5, and recovers a third (33%) of the novel-class gap to the trained state of the art that uses far more supervision. We further show that injecting the few-shot support into the pipeline, as a fusion gate and as a prototypical dense classifier, adds nothing over consistency alone and in fact degrades it through the classifier, which is why the method needs no support at all. On the harder ScanNet++ benchmark, where the dense prior is far weaker on novel classes, the same pipeline nearly doubles novel mIoU (+15.7, from 16.2 to 31.9) at a 1.7 base cost, lifting the harmonic mean from 21.5 to 31.1
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