OP3DSG: Open-Vocabulary Part-Aware 3D Scene Graph Generation for Real-World Environments

Abstract: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.

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2606.29786 [cs.CV]

(or arXiv:2606.29786v2 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.29786 Focus to learn more arXiv-issued DOI via DataCite

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