ProFuse：面向开放词汇3D高斯溅射的高效跨视角上下文融合

ProFuse: Efficient Cross-View Context Fusion for Open-Vocabulary 3D Gaussian Splatting

January 8, 2026

作者: Yen-Jen Chiou, Wei-Tse Cheng, Yuan-Fu Yang

cs.AI

摘要

我们提出了ProFuse——一种基于3D高斯溅射（3DGS）的高效上下文感知开放词汇三维场景理解框架。该流程在直接配准框架下增强了跨视图一致性与掩码内部凝聚力，仅需极少量计算开销且无需渲染监督微调。我们摒弃了预训练3DGS场景的依赖，引入稠密对应关系引导的预配准阶段：通过跨视图聚类联合构建三维上下文提案的同时，以精确几何初始化高斯分布。每个提案携带通过成员嵌入加权聚合获得的全局特征，该特征在直接配准过程中融合至高斯基元，确保多视角下每个图元的语言连贯性。由于预先建立了关联关系，语义融合除标准重建外无需额外优化，模型在保持几何优化能力的同时无需稠密化处理。ProFuse在实现强大开放词汇3DGS理解能力的同时，单场景语义标注耗时约五分钟，较当前最优技术提速两倍。

English

We present ProFuse, an efficient context-aware framework for open-vocabulary 3D scene understanding with 3D Gaussian Splatting (3DGS). The pipeline enhances cross-view consistency and intra-mask cohesion within a direct registration setup, adding minimal overhead and requiring no render-supervised fine-tuning. Instead of relying on a pretrained 3DGS scene, we introduce a dense correspondence-guided pre-registration phase that initializes Gaussians with accurate geometry while jointly constructing 3D Context Proposals via cross-view clustering. Each proposal carries a global feature obtained through weighted aggregation of member embeddings, and this feature is fused onto Gaussians during direct registration to maintain per-primitive language coherence across views. With associations established in advance, semantic fusion requires no additional optimization beyond standard reconstruction, and the model retains geometric refinement without densification. ProFuse achieves strong open-vocabulary 3DGS understanding while completing semantic attachment in about five minutes per scene, which is two times faster than SOTA.