Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding (2404.07989v3)

Abstract: Large foundation models have recently emerged as a prominent focus of interest, attaining superior performance in widespread scenarios. Due to the scarcity of 3D data, many efforts have been made to adapt pre-trained transformers from vision to 3D domains. However, such 2D-to-3D approaches are still limited, due to the potential loss of spatial geometries and high computation cost. More importantly, their frameworks are mainly designed for 2D models, lacking a general any-to-3D paradigm. In this paper, we introduce Any2Point, a parameter-efficient method to empower any-modality large models (vision, language, audio) for 3D understanding. Given a frozen transformer from any source modality, we propose a 3D-to-any (1D or 2D) virtual projection strategy that correlates the input 3D points to the original 1D or 2D positions within the source modality. This mechanism enables us to assign each 3D token with a positional encoding paired with the pre-trained model, which avoids 3D geometry loss caused by the true projection and better motivates the transformer for 3D learning with 1D/2D positional priors. Then, within each transformer block, we insert an any-to-3D guided adapter module for parameter-efficient fine-tuning. The adapter incorporates prior spatial knowledge from the source modality to guide the local feature aggregation of 3D tokens, compelling the semantic adaption of any-modality transformers. We conduct extensive experiments to showcase the effectiveness and efficiency of our method. Code and models are released at https://github.com/Ivan-Tang-3D/Any2Point.

Overview of "Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding"

The paper "Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding" introduces an innovative approach to bridge the gap between multi-modal large foundation models and 3D understanding by leveraging existing pre-trained large models. This paper is motivated by the scarcity of extensive 3D datasets and the challenges associated with adapting 2D-to-3D models, which often encounter spatial geometry loss and computational inefficiency. The proposed framework, Any2Point, aims to facilitate a versatile adaptation of any-modality large models—spanning vision, language, and audio domains—for enhanced 3D recognition and comprehension.

Key Contributions

The authors propose a method emphasizing parameter efficiency, employing a 3D-to-any virtual projection strategy and an any-to-3D guided adapter module within pre-trained transformers. This dual-component framework seeks to maintain the spatial integrity of 3D data while ensuring effective utilization of pre-existing 1D or 2D model parameters.

1. 3D-to-any Virtual Projection: Unlike prior methods that project 3D point clouds into 2D images for input into 2D models—frequently resulting in the loss of spatial information—this virtual projection technique provides a tailored positional mapping to retain critical 3D characteristics. Each 3D point is virtually projected along 1D lines or 2D planes to align with the original positional encodings inherent to the source modality, thereby mitigating geometric loss without necessitating actual dimensional transformation.
2. Any-to-3D Guided Adapter: This component leverages spatial knowledge from the source modality, enhancing local feature aggregation and enabling refined semantic adaptation. By incorporating this adapter within transformer blocks, the method achieves parameter-efficient fine-tuning by dynamically integrating diverse spatial perspectives and improving 3D representation.

Experimental Evaluation

Extensive experiments were conducted to validate the proposed framework's efficacy. Evaluations on 3D object classification tasks, notably on the ScanObjectNN and ModelNet40 datasets, exhibit that Any2Point consistently surpasses existing 3D pre-trained models despite utilizing only a minimal fraction of trainable parameters. The authors highlight significant advancements achieved using pre-trained models from distinct modalities, including DINO V2, CLIP Text Encoder, and ImageBind Audio Encoder, thus affirming the framework's robustness.

Remarkably, the Any2Point approach achieves a 91.9% accuracy on the ScanObjectNN and 94.3% on ModelNet40 when leveraging the CLIP Text Encoder, exhibiting notable improvements over previous state-of-the-art methods. These results underscore the framework’s capacity to draw upon pre-trained knowledge across modalities and efficiently enhance the 3D understanding process.

Implications and Future Developments

The introduction of Any2Point presents notable practical and theoretical implications. Practically, it offers a cost-effective and scalable solution to integrate 3D understanding into existing large models without the necessity for extensive 3D data annotation and collection. Theoretically, it highlights a novel paradigm for cross-modal knowledge transfer, challenging traditional barriers between different data modalities.

Future developments in this field could explore further optimization of the proposed strategies, potentially extending these methods to other complex tasks within 3D domains such as scene understanding, semantic segmentation, and dynamic point cloud processing. Additionally, researchers might investigate the integration of more sophisticated projection techniques and adapter modules to enhance fine-tuning efficiency and model agility across varying datasets. This work represents a meaningful step toward the seamless integration of any-modality knowledge into 3D frameworks, potentially shaping future AI developments in multi-modal interaction and understanding.