mlsanityData to 7 Oct 2026

HyperPlanes: Hypernetwork Approach to Rapid NeRF Adaptation

arXiv 2402.01524 ↗First posted 2 Feb 2024cs.CV cs.LGcited by 1

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Abstract (arXiv)

Neural radiance fields (NeRFs) are a widely accepted standard for synthesizing new 3D object views from a small number of base images. However, NeRFs have limited generalization properties, which means that we need to use significant computational resources to train individual architectures for each item we want to represent. To address this issue, we propose a few-shot learning approach based on the hypernetwork paradigm that does not require gradient optimization during inference. The hypernetwork gathers information from the training data and generates an update for universal weights. As a result, we have developed an efficient method for generating a high-quality 3D object representation from a small number of images in a single step. This has been confirmed by direct comparison with the state-of-the-art solutions and a comprehensive ablation study.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 44 of its 57 references that are in the map. First posted on arXiv 2 Feb 2024.

Builds on

44 of 57 references.

References · 44

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32 more
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