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原文摘要

Scene graphs provide a compact, structured representation of environments by capturing objects and their spatial or semantic relationships in a form suitable for embodied agents. Existing 3D scene graph generation methods often rely on depth sensing at inference, either to associate object instances across views via point cloud overlap or to compute inter-object relations from geometric cues, limiting deployment to platforms with depth sensors. We propose IncR-3DSGG, which builds 3D scene graphs incrementally from posed\footnote{RGB images with camera extrinsics estimated by an off-the-shelf visual SLAM system.} RGB images, eliminating the use of depth sensors at inference. Our method leverages a Parametric Relation Predictor (PRP) that learns to estimate pairwise distances and relative angles between objects from 2D visual features and 2D geometric cues, using 3D geometric supervision available only during training. These learned pairwise measurements are fused across views through Pose Graph Optimization (PGO)~\cite{grisetti2010tutorial} to recover metrically grounded 3D object positions and build a globally consistent 3D scene graph incrementally. On the real-world 3RScan~\cite{wald2019rio} dataset, our method attains $52.2$ mean object recall and $30.8$ mean predicate recall under the 3DSSG~\cite{wald2020learning} evaluation protocol, against $27.0$ and $17.6$ for MonoSSG~\cite{wu2023incremental}, while using no depth or reconstructed geometry at inference. This shows that relations and metrically grounded 3D object representations can be predicted from posed RGB images alone.