让3D高斯点云快速理解开放词汇场景，语义融合快一倍。
ProFuse: Efficient Cross-View Context Fusion for Open-Vocabulary 3D Gaussian Splatting
- 通过跨视角聚类生成3D语义提案，提前建立关联
- 仅用5分钟完成每场景语义附加，速度为当前最优两倍
- 无需重渲染微调，保持几何精度且不增加点数
我们提出ProFuse，一种高效、上下文感知的开放词汇3D场景理解框架，基于3D高斯泼溅（3DGS）。该方法在直接配准设置下提升跨视角一致性与掩码内凝聚力，额外开销极小，且无需渲染监督微调。不同于依赖预训练3DGS场景，我们引入密集对应引导的预配准阶段，以准确几何初始化高斯点，并通过跨视角聚类联合构建3D上下文提案。每个提案通过成员嵌入加权聚合获得全局特征，该特征在直接配准时融合至高斯点，以维持跨视角的逐原语语言一致性。关联预先建立后，语义融合仅需标准重建过程，无需额外优化，模型仍保留几何精化且不进行点云稠密化。ProFuse在约五分钟内完成每场景的语义附着，速度比当前最优方法快两倍。更多细节见项目页面 https://chiou1203.github.io/ProFuse/。
原文摘要 · Abstract (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. Additional details are available at our project page https://chiou1203.github.io/ProFuse/.
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