3D snapshot: Invertible embedding of 3D neural representations in a single image

Lu, Yuqin, Deng, Bailin ORCID: https://orcid.org/0000-0002-0158-7670, Zhong, Zhixuan, Zhang, Tianle, Quan, Yuhui, Cai, Hongmin and He, Shengfeng 2024. 3D snapshot: Invertible embedding of 3D neural representations in a single image. IEEE Transactions on Pattern Analysis and Machine Intelligence 10.1109/TPAMI.2024.3411051

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Abstract

3D neural rendering enables photo-realistic reconstruction of a specific scene by encoding discontinuous inputs into a neural representation. Despite the remarkable rendering results, the storage of network parameters is not transmission-friendly and not extendable to metaverse applications. In this paper, we propose an invertible neural rendering approach that enables generating an interactive 3D model from a single image (i.e., 3D Snapshot). Our idea is to distill a pre-trained neural rendering model (e.g., NeRF) into a visualizable image form that can then be easily inverted back to a neural network. To this end, we first present a neural image distillation method to optimize three neural planes for representing the original neural rendering model. However, this representation is noisy and visually meaningless. We thus propose a dynamic invertible neural network to embed this noisy representation into a plausible image representation of the scene. We demonstrate promising reconstruction quality quantitatively and qualitatively, by comparing to the original neural rendering model, as well as video-based invertible methods. On the other hand, our method can store dozens of NeRFs with a compact restoration network (5MB), and embedding each 3D scene takes up only 160KB of storage. More importantly, our approach is the first solution that allows embedding a neural rendering model into image representations, which enables applications like creating an interactive 3D model from a printed image in the metaverse.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	0162-8828
Funders:	Guangdong Natural Science Funds for Distinguished Young Scholar, National Research Foundation Singapore
Date of First Compliant Deposit:	9 June 2024
Date of Acceptance:	29 May 2024
Last Modified:	17 Jul 2024 11:04
URI:	https://orca.cardiff.ac.uk/id/eprint/169623

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