DepWorldSG: Depth-Aware 3D Semantic Scene Graph Generation via World-Model Priors

We present DeWorldSG, a novel framework that generatesspatio-temporally robust 3D Semantic Scene Graphs from RGB-D se-quences. Existing methods often struggle to construct reliable 3D scenegraphs due to unstable 3D object representations and missing relationscaused by frame-wise inference. DeWorldSG addresses these issues byestimating instance-level geometric 3D Gaussian distributions throughdepth-guided filtering and representing each object as a probabilistic 3Dnode rather than a single projected point. To mitigate relational sparsityfrom frame-wise inference, our framework further aggregates spatiotem-poral evidence across object pairs and refines relations using contextualpriors derived from a world model (V-JEPA 2). Experiments on the3DSSG and ReplicaSSG datasets demonstrate state-of-the-art (SoTA)performance in both object and predicate prediction, while producingtemporally consistent scene structures. In particular, our method im-proves triplet recall by 77.4% and predicate recall by 23.2% over priorSoTA approaches, making it suitable for robotic manipulation and ARapplications. Our code and models are open-sourced.