Hierarchical 3D scene graphs (3DSGs) for indoor robots organize geometric and semantic information across spatial scales, with a room layer that connects object-level perception to room-scale reasoning. Existing systems construct this layer from different spatial substrates (\eg{} place clusters, wall planes, or segmentation outputs), and as a result, room nodes are not evaluated on a common geometric criterion. We present an occupancy-grounded 3DSG pipeline in which room nodes are anchored to tracked free-space regions derived from occupancy decomposition, giving each room an explicit polygonal footprint. We evaluate the pipeline on 12 Matterport3D scenes by matching predicted room polygons to annotated room instances and compare against Hydra, a representative state-of-the-art place-connectivity baseline. The results show that occupancy-grounded anchoring recovers substantially more room instances than place-connectivity construction, at the cost of lower precision, and that wall-accurate room boundaries remain an open problem for both methods. Code is available at https://github.com/crcz25/OccuSG.

翻译：面向室内机器人构建的层次化3D场景图（3DSG）能够跨空间尺度组织几何与语义信息，其中房间层作为连接对象级感知与房间级推理的桥梁。现有系统基于不同空间基元（如位置聚类、墙面或分割输出）构建该层次结构，导致房间节点缺乏统一的几何评估标准。本文提出一种基于占据空间的3DSG管线，通过占据分解追踪的自由空间区域锚定房间节点，为每个房间赋予显式多边形足迹。我们在12个Matterport3D场景中评估该管线，将预测房间多边形与标注房间实例进行匹配，并与代表性最新方法——位置连通性基线Hydra进行对比。结果表明：基于占据空间的锚定方法虽以精度下降为代价，但能恢复出显著多于位置连通性方法的房间实例；而精确的墙体边界建模对两种方法而言仍是开放性问题。代码开源于https://github.com/crcz25/OccuSG。