mlsanityData to 7 Oct 2026

IOVS4NeRF:Incremental Optimal View Selection for Large-Scale NeRFs

arXiv 2407.18611 ↗First posted 26 Jul 2024cs.CVcited by 0

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Abstract (arXiv)

Large-scale Neural Radiance Fields (NeRF) reconstructions are typically hindered by the requirement for extensive image datasets and substantial computational resources. This paper introduces IOVS4NeRF, a framework that employs an uncertainty-guided incremental optimal view selection strategy adaptable to various NeRF implementations. Specifically, by leveraging a hybrid uncertainty model that combines rendering and positional uncertainties, the proposed method calculates the most informative view from among the candidates, thereby enabling incremental optimization of scene reconstruction. Our detailed experiments demonstrate that IOVS4NeRF achieves high-fidelity NeRF reconstruction with minimal computational resources, making it suitable for large-scale scene applications.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 35 of its 62 references that are in the map. First posted on arXiv 26 Jul 2024.

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35 of 62 references.

References · 35

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23 more
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