  Neural radiance fields~(NeRF) have recently been applied to render
large-scale scenes. However, their limited model capacity typically results in
blurred rendering results. Existing large-scale NeRFs primarily address this
limitation by partitioning the scene into blocks, which are subsequently
handled by separate sub-NeRFs. These sub-NeRFs, trained from scratch and
processed independently, lead to inconsistencies in geometry and appearance
across the scene. Consequently, the rendering quality fails to exhibit
significant improvement despite the expansion of model capacity. In this work,
we present global-guided focal neural radiance field (GF-NeRF) that achieves
high-fidelity rendering of large-scale scenes. Our proposed GF-NeRF utilizes a
two-stage (Global and Focal) architecture and a global-guided training
strategy. The global stage obtains a continuous representation of the entire
scene while the focal stage decomposes the scene into multiple blocks and
further processes them with distinct sub-encoders. Leveraging this two-stage
architecture, sub-encoders only need fine-tuning based on the global encoder,
thus reducing training complexity in the focal stage while maintaining
scene-wide consistency. Spatial information and error information from the
global stage also benefit the sub-encoders to focus on crucial areas and
effectively capture more details of large-scale scenes. Notably, our approach
does not rely on any prior knowledge about the target scene, attributing
GF-NeRF adaptable to various large-scale scene types, including street-view and
aerial-view scenes. We demonstrate that our method achieves high-fidelity,
natural rendering results on various types of large-scale datasets. Our project
page: https://shaomq2187.github.io/GF-NeRF/
