ReLaGS：关系语言高斯泼溅
ReLaGS: Relational Language Gaussian Splatting
把多视图 2D 语言特征蒸馏进高斯场景并做高斯剪枝，可在无需场景级训练的情况下得到准确的 3D 物体嵌入，为开放词表 3D 分割与关系检索提供可复用的建图流程。
原文依据 A Gaussian pruning mechanism refines scene geometry, while a robust multi-view language alignment strategy aggregates noisy 2D features into accurate 3D object embeddings.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论在分割、检索与关系理解等任务上实现统一的三维感知与推理仍具挑战性，因为现有方法要么以物体为中心，要么依赖昂贵的训练来完成物体间推理。我们提出一个新框架，无需场景特定训练即可构建分层语言蒸馏的高斯场景及其三维语义场景图。高斯剪枝机制对场景几何进行精化，而鲁棒的多视图语言对齐策略将带噪的二维特征聚合为准确的三维物体嵌入。在该层次结构之上，我们构建了带有视觉语言模型派生标注的开放词表三维场景图，并采用基于图神经网络的关系推理。我们的方法通过联合建模分层语义与物体间/物体内关系，实现高效且可扩展的开放词表三维推理，并在开放词表分割、场景图生成与关系引导检索等任务上得到验证。项目主页：https://dfki-av.github.io/ReLaGS/
英文原文摘要 1054 字符
Achieving unified 3D perception and reasoning across tasks such as segmentation, retrieval, and relation understanding remains challenging, as existing methods are either object-centric or rely on costly training for inter-object reasoning. We present a novel framework that constructs a hierarchical language-distilled Gaussian scene and its 3D semantic scene graph without scene-specific training. A Gaussian pruning mechanism refines scene geometry, while a robust multi-view language alignment strategy aggregates noisy 2D features into accurate 3D object embeddings. On top of this hierarchy, we build an open-vocabulary 3D scene graph with Vision Language-derived annotations and Graph Neural Network-based relational reasoning. Our approach enables efficient and scalable open-vocabulary 3D reasoning by jointly modeling hierarchical semantics and inter/intra-object relationships, validated across tasks including open-vocabulary segmentation, scene graph generation, and relation-guided retrieval. Project page: https://dfki-av.github.io/ReLaGS/