IOVS4NeRF:Incremental Optimal View Selection for Large-Scale NeRFs

TL;DR

IOVS4NeRF introduces an uncertainty-guided incremental view selection framework that enhances large-scale NeRF reconstructions efficiently by selecting the most informative views, reducing computational demands.

Contribution

The paper presents a novel hybrid uncertainty model for incremental view selection, improving large-scale NeRF reconstruction efficiency and quality.

Findings

Achieves high-fidelity NeRF with minimal resources

Effective view selection reduces computational load

Applicable to various NeRF implementations

Abstract

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.