This paper offers an extensive review of the methodologies that enable LLMs to handle 3D spatial data. The study covers various data representations like point clouds and Neural Radiance Fields (NeRFs), exploring their integration with LLMs. The capabilities such as in-context learning and extensive world knowledg](https://github.com/ActiveVisionLab/Awesome-LLM-3D) are discussed, particularly for spatial tasks in embodied AI systems.

Key Highlights:

• Explores diverse 3D data representations and their integration with LLMs.
  • Points out current challenges and calls for novel approaches in the field.
  • Establishes a detailed framework for 3D LLM integration that could guide future research.

Further Research Potentials:

• Potential to reshape how AI systems interact with and understand the physical world.
• Encourages further exploration into the synergy between 3D modeling and language models.