  In this paper, we present a novel and effective framework, named 4K-NeRF, to
pursue high fidelity view synthesis on the challenging scenarios of ultra high
resolutions, building on the methodology of neural radiance fields (NeRF). The
rendering procedure of NeRF-based methods typically relies on a pixel-wise
manner in which rays (or pixels) are treated independently on both training and
inference phases, limiting its representational ability on describing subtle
details, especially when lifting to a extremely high resolution. We address the
issue by exploring ray correlation to enhance high-frequency details recovery.
Particularly, we use the 3D-aware encoder to model geometric information
effectively in a lower resolution space and recover fine details through the
3D-aware decoder, conditioned on ray features and depths estimated by the
encoder. Joint training with patch-based sampling further facilitates our
method incorporating the supervision from perception oriented regularization
beyond pixel-wise loss. Benefiting from the use of geometry-aware local
context, our method can significantly boost rendering quality on high-frequency
details compared with modern NeRF methods, and achieve the state-of-the-art
visual quality on 4K ultra-high-resolution scenarios. Code Available at
\url{https://github.com/frozoul/4K-NeRF}
