Recent attempts to transfer features from 2D Vision-Language Models (VLMs) to 3D semantic segmentation expose a persistent trade-off. Directly projecting 2D features into 3D yields noisy and fragmented predictions, whereas enforcing geometric coherence necessitates costly training pipelines and large-scale annotated 3D data. We argue that this limitation stems from the dominant segmentation-and-matching paradigm, which fails to reconcile 2D semantics with 3D geometric structure. The geometric cues are not eliminated during the 2D-to-3D transfer but remain latent within the noisy and view-aggregated features. To exploit this property, we propose GeoPurify that applies a small Student Affinity Network to purify 2D VLM-generated 3D point features using geometric priors distilled from a 3D self-supervised teacher model. During inference, we devise a Geometry-Guided Pooling module to further denoise the point cloud and ensure the semantic and structural consistency. Benefiting from latent geometric information and the learned affinity network, GeoPurify effectively mitigates the trade-off and achieves superior data efficiency. Extensive experiments on major 3D benchmarks demonstrate that GeoPurify achieves or surpasses state-of-the-art performance while utilizing only about 1.5% of the training data.

翻译：近期将二维视觉语言模型特征迁移至三维语义分割的尝试揭示了一个持续存在的权衡问题。直接将二维特征投影到三维空间会产生噪声且碎片化的预测，而强制几何一致性则需要昂贵的训练流程和大规模标注的三维数据。我们认为这一局限源于主流的“分割-匹配”范式未能将二维语义与三维几何结构相协调。几何线索在二维到三维的迁移过程中并未被消除，而是潜藏于经过视图聚合的噪声特征中。为利用这一特性，我们提出GeoPurify框架，通过一个小型学生亲和力网络，利用从三维自监督教师模型蒸馏得到的几何先验来纯化二维视觉语言模型生成的三维点特征。在推理阶段，我们设计了几何引导池化模块以进一步去噪点云并确保语义与结构的一致性。得益于潜在的几何信息与习得的亲和力网络，GeoPurify有效缓解了上述权衡问题并实现了卓越的数据效率。在主流三维基准测试上的大量实验表明，GeoPurify仅需约1.5%的训练数据即可达到或超越当前最优性能。