发表机构
Laboratory for Information & Decision Systems (LIDS), Massachusetts Institute of Technology; Department of Electrical and Computer Engineering, Oakland University（信息与决策系统实验室（LIDS），麻省理工学院; 奥克兰大学电气与计算机工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对机器人部分观测环境的场景预测问题，提出自上而下框架合成含房间层和物体层的3D场景图，用混合域图扩散模型等方法，相比其他方法对分布外部分平面图泛化性更好，能在真实场景预测。
AI 中文摘要
生成逼真的3D室内场景在计算机视觉和机器人领域越发受到关注。现有方法多聚焦单房间内物体布局生成，高层场景结构生成探索不足。本文针对机器人探索部分环境后预测未探索部分以支持下游任务的情况，提出自上而下框架合成层次3D场景图，含房间层和物体层。房间层用新型混合域图扩散模型，物体层集成现有模型。在标准基准上与其他方法对比，结果表明该方法对分布外部分平面图泛化性更好，还在真实场景展示了预测能力。
英文摘要
Generating realistic 3D indoor scenes is an area of growing interest in computer vision and robotics. Existing methods, often motivated by applications such as interior design, generally focus on object layout generation within a single room. The generation of high-level scene structure, such as room-level layout and traversability, remains underexplored despite its importance for robotics applications. In this paper, we consider the case where a robot has explored part of an environment and needs to predict the unexplored parts to support downstream tasks such as exploration or object search. We propose a top-down framework for synthesizing hierarchical 3D scene graphs, including a room layer -- describing the floor plan and traversability -- and an object layer modeling object layouts within each room. For the room layer, we propose a novel mixed-domain graph diffusion model jointly predicting room categories, floor boundaries, and traversability between rooms. Via corruption and masking, this model supports partial constraints such as incomplete floor plans, avoiding the need for partially observed training data. For the object layer, we integrate an existing mixed discrete-continuous diffusion model for joint prediction of object categories, locations, sizes, and orientations within each room given the floor plan. We compare our method with state-of-the-art occupancy-based and LLM-based floor plan generation methods on a standard benchmark. Compared with an occupancy-based learning baseline, our method generalizes substantially better to out-of-distribution partial floor plans. We also demonstrate our integrated prediction pipeline on real-world scenes from robot-collected data, enabling prediction beyond explored areas.
CommentsAccepted at IROS 2026. Main paper: 8 pages, 3 figures, 3 tables. Includes a supplementary appendix
