用占据栅格定位房间，让3D场景图更准。
Occupancy-Grounded Room Segmentation for Hierarchical 3D Scene Graphs
- 以占据分解的自由空间为依据定义房间边界
- 在12个Matterport3D场景中多恢复40%以上房间实例
- 适合需要精准房间语义的机器人导航任务
室内机器人使用的层次化3D场景图（3DSG）将几何与语义信息按空间尺度组织，其中房间层连接物体级感知与房间级推理。现有系统基于不同空间基础（如位置聚类、墙平面或分割结果）构建该层，导致房间节点缺乏统一的几何评价标准。本文提出一种基于占据栅格的3DSG流程，将房间节点锚定在通过占据分解追踪到的自由空间区域上，使每个房间具有明确的多边形足迹。在12个Matterport3D场景上，通过匹配预测房间多边形与标注实例进行评估，并与代表性的先进基准Hydra（基于位置连通性）对比。结果表明，占据锚定方法显著恢复更多房间实例，但精度较低；而墙级精确的房间边界对两种方法仍是未解难题。代码已公开于https://github.com/crcz25/OccuSG。
原文摘要 · Abstract (English)
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.
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