3Dシーングラフにおける意味的不確実性の階層的集約
Hierarchical Aggregation of Semantic Uncertainty in 3D Scene Graphs
既存の3Dシーングラフが持つ検出器信頼度と埋め込みから、物体エントリの正しさを確率化し、包含階層を通じて部屋レベルの信念に伝播させる枠組みを提案。追加の知覚や学習なしで物体検索と部屋レベルの主張の精度を向上。
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著者: Carlos Cueto Zumaya, Iacopo Catalano, Wallace Moreira Bessa, Julio A. Placed
分類: cs.CV
原文アブストラクト
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.
