Global-Local Monte Carlo Tree Search in Vision-Language Models for Text-to-3D Indoor Scene Generation
Large Vision-Language Models have achieved significant reasoning performance in various tasks. However, there are few studies on text-to-3D indoor scene generation with LVLMs. The main challenge is that prevailing LVLM-based methods employ chain-of-thought sequential decision mechanisms that cannot revise earlier decisions, causing error propagation. In this paper, we consider the task as a planning problem constrained by spatial and layout commonsense. To solve this problem, we model it as a tree search problem with global and local trees, which differs from existing sequential decision-making approaches. In the global tree, we place each object iteratively and explore multiple attempts ...
| Authors | Mengshi Qi, Wei Deng, Xianlin Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 June 2026 |
| Last updated at source | 5 June 2026 |
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