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-    <title>inr2vec</title>
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                 <h3>
                     Abstract
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-                <image src="img/teaser.svg" class="image_center_100" alt="Applications of our framework"></image>
                 <p class="text-justify">
                     Neural Radiance Fields (NeRFs) have emerged as a standard framework for representing 3D scenes and objects, introducing a novel data type for information exchange and storage. Concurrently, significant progress has been made in multimodal representation learning for text and image data. This paper explores a novel research direction that aims to connect the NeRF modality with other modalities, similar to established methodologies for images and text. To this end, we propose a simple framework that exploits pre-trained models for NeRF representations alongside multimodal models for text and image processing. Our framework learns a bidirectional mapping between NeRF embeddings and those obtained from corresponding images and text. This mapping unlocks several novel and useful applications, including NeRF zero-shot classification and NeRF retrieval from images or text.
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+                <image src="img/teaser.png" class="image_center_100" alt="Applications of our framework"></image>
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