Global Graph-Validated Optimization for VLM-based 3D Indoor Scene Generation

• URL: http://arxiv.org/abs/2608.03064v1
• Date: Tue, 04 Aug 2026 03:24:09 GMT
• Title: Global Graph-Validated Optimization for VLM-based 3D Indoor Scene Generation
• Abstract summary: We study open-vocabulary 3D indoor layout generation, synthesizes diverse physically plausible scenes from unlabeled 3D language instructions.<n>Our method achieves state-of-the-art performance in open-vocabulary 3D indoor layout generation.
• Score: 15.117449876348774
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We study open-vocabulary 3D indoor layout generation, which synthesizes diverse and physically plausible scenes from unlabeled 3D assets using free-form language instructions. Recent methods leverage large language models (LLMs) and vision-language models (VLMs) to generate structured scenes from text. However, most model inter-asset relations implicitly or rely on local pairwise constraints and local optimization. These formulations are poorly aligned with the global, highly non-convex layout space, often yielding locally plausible yet globally inconsistent or physically infeasible scenes. We address this problem with a graph-based intermediate representation that separates semantic coherence from physical feasibility, together with a hybrid search-and-refinement strategy. First, Global Semantic Verification (GSV) represents scenes as structured graphs and enforces semantic constraints through rule-based verification. This explicit validation removes contradictory configurations and produces a globally consistent semantic scaffold. Second, Global Physical Feasibility Search (GPFS) combines evolutionary search for global exploration with gradient-based refinement for local exploitation. It reduces dependence on VLM-proposed initialization and improves robustness in non-convex and discontinuous feasible spaces. Together, GSV and GPFS move layout generation beyond local relational modeling and initialization-sensitive optimization toward globally consistent reasoning and search. Experiments show that our method achieves state-of-the-art performance in open-vocabulary 3D indoor layout generation, improving both semantic consistency and physical plausibility.

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