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arXiv · 2609.22351

Hierarchical Aggregation of Semantic Uncertainty in 3D Scene Graphs

Carlos Cueto Zumaya·Iacopo Catalano·Wallace Moreira Bessa·Julio A. Placed
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Abstract

Open-vocabulary 3D Scene Graphs (3DSGs) ground each object node in a vision-language embedding, yet they record every entry as equally certain, so a robot querying the map cannot tell which of its entries are unreliable. Estimators of semantic uncertainty could supply that distinction, but they require repeated sampling of a model, training, or held-out labels, none of which are available to a deployed system at query time. We present a framework that exploits the detector confidence and the embeddings a 3DSG already stores, converts them into a probability that an entry is correct, and propagates that probability through the containment hierarchy into a belief that a room contains a queried class. Four signals, each paired with the object-level error it indicates, are converted to probabilities at the logit scale learned by the vision-language model and combined in closed form with no additional perception or training. Objects sharing a detector and a vocabulary fail together, so the framework aggregates them in the fully correlated limit, where an aggregation under independence would treat one repeated error as repeated evidence. Evaluated on HM3DSem against a state-of-the-art 3DSG system, the framework improves object retrieval and lowers the error of the room-level assertions of the graph it reads.

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Carlos Cueto Zumaya, Iacopo Catalano, Wallace Moreira Bessa, Julio A. Placed. 2026-09-17. Hierarchical Aggregation of Semantic Uncertainty in 3D Scene Graphs. https://arxiv.org/abs/2609.22351

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