Strata3D: Efficient and Accurate Agentic Indoor Scene Generation with Layered Floor Plans

Simulated indoor scenes are the training ground for embodied agents, and the scenes that matter most are dense: tens of objects stacked on, tucked under and hung from a few pieces of furniture. Recent agentic generators place objects directly in 3D and discover physical and functional constraints by rendering, critiquing and revising — a search that grows expensive as rooms become dense and still leaves many objects in collision. Our key insight is that many of these constraints are not three-dimensional: whether objects overlap, whether a doorway stays clear and whether every object can be reached are properties of the floor plan, provided the plan records which objects may overlap. We introduce Strata3D, a layout-first framework built on a layered floor plan, in which each object is a footprint on one of five vertical layers. The layers turn these constraints into exact, deterministic checks whose findings guide a language model in revising its plan; the validated plan is then lifted to 3D as a contract that the retrieved assets must honour. On dense prompts, against SceneCode and SceneSmith run to completion, Strata3D improves object-count accuracy by more than ten points, halves the share of colliding objects and wins 41 of 46 blind pairwise comparisons, while using two orders of magnitude fewer tokens. Ablations attribute each gain to the component that targets it, and the generated scenes can be used directly as mobile-manipulation environments.