ProFuse: 高效的跨视角上下文融合用于开放词汇3D高斯点云渲染
ProFuse: Efficient Cross-View Context Fusion for Open-Vocabulary 3D Gaussian Splatting
AI总结：
ProFuse通过高效的跨视角上下文融合方法，实现开放词汇3D高斯点云渲染的快速语义附着。
摘要：
我们提出了ProFuse，一种高效的上下文感知框架，用于开放词汇3D场景理解，结合3D高斯点云渲染（3DGS）。该流程在直接注册设置中增强了跨视角一致性及内部掩码凝聚力，添加了极小的开销且不需要渲染监督的微调。不同于依赖预训练的3DGS场景，我们引入了一个密集对应关系引导的预注册阶段，该阶段通过跨视角聚类共同构建3D上下文提案，同时初始化高斯分布以获得准确的几何信息。每个提案通过加权聚合成员嵌入获得全局特征，并在直接注册过程中将该特征融合到高斯分布上，以保持不同视角下的每个原始元素语言一致性。在建立好关联之后，语义融合不需要额外的优化，仅需标准重建即可完成。ProFuse在约五分钟左右完成每个场景的语义附着，比SOTA快两倍。更多细节请参见我们的项目页面https://chiou1203.github.io/ProFuse/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
