More Text, Less Point: Towards 3D Data-Efficient Point-Language Understanding (2408.15966v2)

Abstract: Enabling LLMs to comprehend the 3D physical world remains a significant challenge. Due to the lack of large-scale 3D-text pair datasets, the success of LLMs has yet to be replicated in 3D understanding. In this paper, we rethink this issue and propose a new task: 3D Data-Efficient Point-Language Understanding. The goal is to enable LLMs to achieve robust 3D object understanding with minimal 3D point cloud and text data pairs. To address this task, we introduce GreenPLM, which leverages more text data to compensate for the lack of 3D data. First, inspired by using CLIP to align images and text, we utilize a pre-trained point cloud-text encoder to map the 3D point cloud space to the text space. This mapping leaves us to seamlessly connect the text space with LLMs. Once the point-text-LLM connection is established, we further enhance text-LLM alignment by expanding the intermediate text space, thereby reducing the reliance on 3D point cloud data. Specifically, we generate 6M free-text descriptions of 3D objects, and design a three-stage training strategy to help LLMs better explore the intrinsic connections between different modalities. To achieve efficient modality alignment, we design a zero-parameter cross-attention module for token pooling. Extensive experimental results show that GreenPLM requires only 12% of the 3D training data used by existing state-of-the-art models to achieve superior 3D understanding. Remarkably, GreenPLM also achieves competitive performance using text-only data. The code and weights are available at: https://github.com/TangYuan96/GreenPLM.

Insights into 3D Data-Efficient Point-Language Understanding

The paper introduces a novel approach to 3D understanding by proposing the task of 3D Data-Efficient Point-Language Understanding, aiming to leverage the capabilities of LLMs with minimal reliance on 3D point cloud and text data pairs. The authors present GreenPLM, a framework that enhances LLM alignment through the expansion of text data to compensate for the scarcity of 3D data.

Framework and Methodology

GreenPLM operates by creating a seamless transition from 3D point cloud data to text space using pre-trained encoders, drawing inspiration from the CLIP model's image-text alignment. The approach involves three stages:

1. Data Utilization: The GreenPLM framework compensates for limited 3D data by leveraging an extensive dataset of free-text descriptions. This includes the T3D dataset, comprising 6M textual descriptions and conversations about 3D objects, generated using open-source models like Qwen2-72B-Instruct.
2. Three-Stage Training Strategy: The training begins with aligning the text encoder with the LLM using abundant textual data, progressing to minimal 3D data usage to complete the alignment.
   • Stage I focuses on building basic alignment between the textual encoder and the LLM.
   • Stage II refines this alignment by incorporating complex textual data, employing LoRA to fine-tune the model's comprehension capabilities.
   • Stage III introduces the minimal 3D data to finalize the point cloud and LLM connection, using a zero-parameter cross-attention module known as 0M-Pooling for efficient token pooling.
3. Cross-Attention Mechanisms: The authors propose a novel zero-parameter cross-attention module to optimize token pooling during 3D data processing, ensuring the model remains efficient and capable of robust performance with reduced resource demands.

Experimental Outcomes

The GreenPLM framework demonstrates remarkable efficiency, achieving comparable, if not superior, results to previous state-of-the-art models using significantly less 3D data. Notably, the model performs effectively even with text-only data, highlighting its robust design for data-efficient understanding across different modalities. The paper presents robust evaluation metrics, such as the Accuracy-to-3D-Data Ratio (A3DR), emphasizing the model's high efficiency in utilizing available data.

Implications and Future Directions

The research indicates profound implications for the future of multimodal understanding in AI. By shifting the dependency away from expensive and scarce 3D datasets to more accessible text-based information, GreenPLM proposes a viable path toward scalable and resource-efficient 3D understanding models. This approach can potentially democratize access to advanced AI capabilities, notably in fields like autonomous systems and robotics where 3D comprehension is crucial. Future research could explore expanding this methodology to broader and more complex environments, enhancing model robustness and generalization further.

The paper sets a foundation for advancing multimodal AI systems that effectively integrate language and 3D spatial comprehension, marking a significant step towards achieving comprehensive AI understanding of the physical world with limited data resources.