A Novel Framework for Realistic 3D Scene Regeneration with Graph of Thoughts

In embodied intelligence applications, highly realistic 3D scenes lay the foundation for perception and decision-making, while 3D scene regeneration creates more coherent and personalized virtual spaces, facilitating more efficient task adaptation and agent training. To address this, we propose a reasoning framework based on the Graph of Thoughts (GoT), which enhances the prompting capabilities of large language models (LLM) and integrates a synergistic mechanism of retrospective memory and feedback loops into the regeneration process. During the initial generation phase, we retain the Holodeck paradigm, combining LLM-driven scene design inferences with the spatial layout of 3D assets from Objaverse. In the regeneration phase, dynamic feedback loops trigger backtracking of reasoning memory to adjust relevant elements according to evolving requirements, while maintaining stability and consistency in unrelated elements, ensuring the scene's overall coherence. We conduct both subjective and objective experiments to validate the effectiveness of this framework, demonstrating significant improvements in 3D scene generation.