MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors

TL;DR

MiniGPT-3D introduces an efficient method for aligning 3D point clouds with large language models by leveraging 2D priors, achieving state-of-the-art results with significantly reduced training costs and parameters.

Contribution

The paper proposes a novel 4-stage training strategy, a mixture of query experts module, and parameter-efficient fine-tuning techniques to develop MiniGPT-3D, a cost-effective 3D-LLM with superior performance.

Findings

Achieves SOTA on 3D classification and captioning

Reduces training time to 27 hours on a single GPU

Gains 8.12 score increase over ShapeLLM-13B in object captioning

Abstract

Large 2D vision-language models (2D-LLMs) have gained significant attention by bridging Large Language Models (LLMs) with images using a simple projector. Inspired by their success, large 3D point cloud-language models (3D-LLMs) also integrate point clouds into LLMs. However, directly aligning point clouds with LLM requires expensive training costs, typically in hundreds of GPU-hours on A100, which hinders the development of 3D-LLMs. In this paper, we introduce MiniGPT-3D, an efficient and powerful 3D-LLM that achieves multiple SOTA results while training for only 27 hours on one RTX 3090. Specifically, we propose to align 3D point clouds with LLMs using 2D priors from 2D-LLMs, which can leverage the similarity between 2D and 3D visual information. We introduce a novel four-stage training strategy for modality alignment in a cascaded way, and a mixture of query experts module to adaptively aggregate features with high efficiency. Moreover, we utilize parameter-efficient fine-tuning methods LoRA and Norm fine-tuning, resulting in only 47.8M learnable parameters, which is up to 260x fewer than existing methods. Extensive experiments show that MiniGPT-3D achieves SOTA on 3D object classification and captioning tasks, with significantly cheaper training costs. Notably, MiniGPT-3D gains an 8.12 increase on GPT-4 evaluation score for the challenging object captioning task compared to ShapeLLM-13B, while the latter costs 160 total GPU-hours on 8 A800. We are the first to explore the efficient 3D-LLM, offering new insights to the community. Code and weights are available at https://github.com/TangYuan96/MiniGPT-3D.