OP3DSG：面向真实环境的开放词汇部件感知3D场景图生成
OP3DSG: Open-Vocabulary Part-Aware 3D Scene Graph Generation for Real-World Environments
AI总结：
提出OP3DSG框架，通过部件感知检测与融合、几何初始化先验图及LLM精炼，实现开放词汇下对象、交互部件、空间/功能关系及可供性的统一建模，在UniGraph3D基准上达到最优性能。
摘要：
3D场景图（3DSG）提供了3D环境的紧凑且结构化抽象。尽管基础模型的进步使得开放词汇的3DSG生成成为可能，现有方法仍以对象为中心并编码有限的关联信息——限制了它们在需要细粒度理解的真实场景中的适用性。我们提出OP3DSG，一个开放词汇的部件感知3DSG生成框架，构建统一图，联合建模对象、交互部件、空间关系、功能关系和可供性。OP3DSG集成了对象-部件知识引导的检测与部件感知的3D融合，以保留小型且与交互相关的组件，并采用几何初始化的先验图结合基于LLM的精炼，减少虚假关系预测，同时实现高效的图构建。为系统评估统一3D场景图构建，我们引入UniGraph3D，一个专为部件感知感知和多级关系推理设计的基准。实验结果表明，OP3DSG达到了最先进的性能，并展示了其在多样化真实世界机器人任务中作为感知主干的有效性。
英文摘要：
3D scene graphs (3DSGs) provide a compact and structured abstraction of 3D environments. Although advances in foundation models have enabled open-vocabulary 3DSG generation, existing approaches remain object-centric and encode limited relational information -- restricting their applicability in real-world scenarios that require fine-grained understanding. We propose OP3DSG, an open-vocabulary part-aware 3DSG generation framework that constructs unified graphs that jointly model objects, interactive parts, spatial relations, functional relations, and affordances. OP3DSG integrates object-part knowledge-guided detection with part-aware 3D fusion to preserve small and interaction-relevant components, and employs a geometry-initialized prior graph with LLM-based refinement to reduce spurious relational predictions while enabling efficient graph construction. To systematically evaluate unified 3D scene graph construction, we introduce UniGraph3D, a benchmark designed for part-aware perception and multi-level relational reasoning. Experimental results show that OP3DSG achieves state-of-the-art performance and demonstrates its effectiveness as a perception backbone in diverse real-world robotics tasks.
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