层次化和整体化的开放词汇功能3D场景图用于室内空间
Hierarchical and Holistic Open-Vocabulary Functional 3D Scene Graphs for Indoor Spaces
AI总结：
本文提出一种开放词汇管道，结合2D视觉定位和3D图优化，解决小规模密集相似实例的场景图推理问题，通过时间图优化和全局层次塑造提升室内空间的功能3D场景图生成能力。
摘要：
功能3D场景图提供了一种灵活的3D场景理解和机器人操作的表示方法，由物体节点、交互元素和功能关系边定义。然而，由于现有基准覆盖有限和先前管道设计过于简单，其潜力尚未被充分挖掘。因此，本文通过引入密集的桌面上物体和显式的多级功能关系扩展基准覆盖。这种扩展引入了关键挑战，包括小规模、密集和相似实例的处理，关系推理中缺乏视觉锚点，跨帧融合中的实例混淆，以及动态视角下的属性不确定性。为了解决这些问题，我们提出了一种基于2D视觉定位和3D图优化的开放词汇管道。具体而言，我们从2D视觉证据中锚定细粒度的功能边，并使用多个线索在3D中跨帧关联节点。此外，边关联被公式化为时间图优化，整合证据积累、熵正则化和时间平滑，以稳健地确定每个节点的功能连接。最后，通过全局层次塑造恢复层次图结构。大量实验表明，所提方法能够在具有挑战性的现实场景中可靠地推断功能3D场景图，从而进一步解锁其在实际应用中的潜力。
英文摘要：
Functional 3D scene graphs offer a versatile and flexible representation for 3D scene understanding and robotic manipulation, defined by object nodes, interactive elements, and functional relationship edges. However, their potential remains underexplored due to the limited coverage of existing benchmarks and the overly straightforward design of previous pipelines, which primarily focus on large-scale furniture but lack of hierarchical structures. Therefore, in this work, we extend the benchmark coverage by introducing dense tabletop objects and explicit multi-level functional relationships. This expansion introduces critical challenges involving small-scale, dense, and similar instances, with lack of visual anchoring in relational reasoning, instance confusion during cross-frame fusion, and attribution uncertainty under dynamic viewpoints. To address these issues, we propose an open-vocabulary pipeline based on 2D visual grounding and 3D graph optimization. Specifically, we anchor fine-grained functional edges from 2D visual evidence, and associate nodes across frames in 3D using multiple cues. Furthermore, edge association is formulated as temporal graph optimization, integrating evidence accumulation, entropy regularization, and temporal smoothing to robustly determine the functional connections of each node. Finally, global hierarchy shaping is performed to recover the hierarchical graph structure. Extensive experiments demonstrate that the proposed method can reliably infer functional 3D scene graphs in challenging real-world scenes, thereby further unlocking their potential for practical applications.
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。
