Neural Radiance Fields, or NeRFs, have revolutionized the way we represent 3D scenes and objects, introducing a unique data type for information exchange and storage. In parallel, significant advancements have been made in multimodal representation learning, particularly for text and image data. This paper explores an exciting new research direction that aims to bridge the gap between NeRFs and other modalities, much like the established connections between images and text.

The authors propose a simple yet effective framework that combines pre-trained models for NeRF representations with multimodal models designed for text and image processing. By learning a bidirectional mapping between NeRF embeddings and corresponding image and text embeddings, the framework unlocks a range of novel applications.

This mapping enables tasks such as zero-shot classification of NeRFs and retrieval of NeRFs based on associated images or text descriptions. The proposed method builds upon established techniques in multimodal representation learning, leveraging the power of pre-trained models to enhance the capabilities of NeRFs.

The paper contributes to the growing field of multimodal representation learning by offering a novel approach to integrating NeRFs with other modalities. By connecting these different data types, the framework expands the potential for information exchange and opens up new possibilities for applications that require a rich understanding of 3D scenes and objects.

The authors demonstrate the effectiveness of their framework through experimental results, showcasing its ability to facilitate effective information retrieval and classification across modalities. This work paves the way for further exploration and innovation in the realm of multimodal representation learning, with NeRFs playing a central role in bridging the gap between 3D data and other forms of media.