IOVS4NeRF:Incremental Optimal View Selection for Large-Scale NeRFs
This addresses the computational bottleneck in large-scale NeRF scene reconstruction, making it more efficient for practical applications.
The paper tackles the problem of large-scale Neural Radiance Fields (NeRF) reconstructions requiring extensive image datasets and computational resources by introducing IOVS4NeRF, a framework that uses an uncertainty-guided incremental optimal view selection strategy, achieving high-fidelity reconstruction with minimal computational resources.
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.