Orchestrating Spatial Semantics via a Zone-Graph Paradigm for Intricate Indoor Scene Generation

TL;DR

ZoneMaestro introduces a zone-graph based framework for robust indoor scene synthesis, effectively handling complex non-convex spaces and dense spatial relations through semantic and topological reasoning.

Contribution

The paper presents a novel zone-graph paradigm, a large-scale annotated dataset, and an optimization strategy for improved indoor scene generation and spatial reasoning.

Findings

ZoneMaestro outperforms baselines in structural coherence.

It effectively manages complex dense spatial relations.

The approach enhances semantic richness and geometric validity.

Abstract

Autonomous 3D indoor scene synthesis breaks down in non-convex rooms with tightly coupled spatial constraints. Data-driven generators lack topological priors for long-horizon planning, while iterative agents fragment semantics and become geometrically brittle. We present ZoneMaestro, a unified framework that shifts the paradigm from object-centric synthesis to Zone-Graph Orchestration. By internalizing a novel zone-based logic, ZoneMaestro translates high-level semantic intent into functional zones and topological constraints, enabling robust adaptation to diverse architectural forms. To support this, we construct Zone-Scene-10K, a large-scale dataset enriched with explicit Zone-Graph annotations. We further introduce an Alternating Alignment Strategy that cycles between reasoning internalization and Zone-Aware Group Relative Policy Optimization (Z-GRPO), effectively reconciling the tension between semantic richness and geometric validity without relying on external physics engines. To rigorously evaluate spatial intelligence beyond convex primitives, we formally define the task of Intricate Spatial Orchestration and release SCALE, a stress-test benchmark for irregular indoor scenarios with complex, dense spatial relations. Extensive experiments demonstrate that ZoneMaestro resolves the density-safety dichotomy, significantly outperforming state-of-the-art baselines in both structural coherence and intent adherence.