  Urban-level three-dimensional reconstruction for modern applications demands
high rendering fidelity while minimizing computational costs. The advent of
Neural Radiance Fields (NeRF) has enhanced 3D reconstruction, yet it exhibits
artifacts under multiple viewpoints. In this paper, we propose a new NeRF
framework method to address these issues. Our method uses image content and
pose data to iteratively plan the next best view. A crucial aspect of this
method involves uncertainty estimation, guiding the selection of views with
maximum information gain from a candidate set. This iterative process enhances
rendering quality over time. Simultaneously, we introduce the Vonoroi diagram
and threshold sampling together with flight classifier to boost the efficiency,
while keep the original NeRF network intact. It can serve as a plug-in tool to
assist in better rendering, outperforming baselines and similar prior works.
