KNA-SG: Keyframe–Node-Associated Open-Vocabulary 3D Scene Graphs from RGB Sequences

Abstract

3D scene graphs organize objects and their relationships in a scene into structured representations, providing an interpretable and queryable foundation for relational reasoning and object grounding. Existing open-vocabulary 3D scene graph construction methods primarily focus on object-level feature representation and open-ended edge reasoning. However, they often lack explicit and retrievable associations between object nodes and keyframes, making it difficult to recall relevant visual evidence for target disambiguation and relationship verification in complex queries. Moreover, pre-constructed edges are inherently limited in their ability to cover the diverse linguistic expressions encountered in downstream tasks. To address these limitations, we propose KNA-SG, a framework for constructing open-vocabulary 3D scene graphs from RGB sequences with explicit keyframe–node associations. Built upon instance-grounded 3D reconstruction, KNA-SG represents each object instance as a graph node and uses a unique instance identifier to associate the node with the keyframes in which the instance is observed. The ID-annotated keyframes guide MLLM-based open-vocabulary semantic parsing, enabling semantic attributes to be assigned to these graph nodes. This design transforms keyframes into retrievable visual evidence for target disambiguation and relationship verification during query reasoning. Verified relationships are further written back into the scene graph as reusable relational memory to support subsequent queries. To ensure the effectiveness of selected keyframes, we design a two-stage keyframe selection strategy that combines visual quality assessment with semantic redundancy removal, preserving a set of clear keyframes that provide comprehensive scene coverage. Experimental results show that KNA-SG outperforms existing methods on open-vocabulary 3D semantic segmentation and 3D object grounding tasks.