Spatial reasoning in large-scale 3D environments remains challenging for current vision--language models, which are typically constrained to room-scale scenarios. We formalize Active House-Scale Spatial Reasoning (AHSR), a new paradigm in which a model reasons over a pre-built house-scale 3D map via virtual spatial tool invocations to answer spatial questions, without exhaustive scene-wide processing. To support AHSR research, we introduce H$^2$U3D (Holistic House Understanding in 3D), the first benchmark targeting house-scale 3D scene understanding, featuring environments with an average aggregate floor area of 250.8 m$^2$ and up to three floors, together with hierarchical coarse-to-fine visual representations. Building on H$^2$U3D, we propose SpatialReasoner, an AHSR framework trained via supervised fine-tuning with self-correction, followed by reinforcement learning with a task-aware adaptive exploration reward. SpatialReasoner achieves state-of-the-art performance on H$^2$U3D with 64.9% overall accuracy, outperforming strong baselines including GPT-5.4 and Gemini-3.5-Flash, and generalizes effectively to MT-HM3D and HM-EQA. These results demonstrate the clear advantage of active map-directed exploration over passive scene-wide processing in house-scale 3D understanding.

翻译：暂无翻译