MiniGPT-3D offers an efficient method for aligning 3D point clouds with large language models using 2D priors. The approach aims to connect 3D point clouds with LLMs in a more resource-efficient manner compared to existing methods. MiniGPT-3D leverages parameter-efficient fine-tuning techniques, resulting in significantly fewer learnable parameters. By incorporating 2D-LLM knowledge, MiniGPT-3D enhances the understanding of 3D objects and enables reasoning in 3D space.

The method involves four training stages, each focusing on different aspects of establishing 3D-language knowledge. MiniGPT-3D fine-tunes specific modules and minimizes the impact of data distribution changes to ensure stable semantic context for efficient learning. The training objective is to minimize the discrepancy between predicted and true probability distributions at each token position, enhancing the model's ability to generate accurate text responses based on input point clouds and text instructions.