  Recent progress in large-scale scene rendering has yielded Neural Radiance
Fields (NeRF)-based models with an impressive ability to synthesize scenes
across small objects and indoor scenes. Nevertheless, extending this idea to
large-scale aerial rendering poses two critical problems. Firstly, a single
NeRF cannot render the entire scene with high-precision for complex large-scale
aerial datasets since the sampling range along each view ray is insufficient to
cover buildings adequately. Secondly, traditional NeRFs are infeasible to train
on one GPU to enable interactive fly-throughs for modeling massive images.
Instead, existing methods typically separate the whole scene into multiple
regions and train a NeRF on each region, which are unaccustomed to different
flight trajectories and difficult to achieve fast rendering. To that end, we
propose Aerial-NeRF with three innovative modifications for jointly adapting
NeRF in large-scale aerial rendering: (1) Designing an adaptive spatial
partitioning and selection method based on drones' poses to adapt different
flight trajectories; (2) Using similarity of poses instead of (expert) network
for rendering speedup to determine which region a new viewpoint belongs to; (3)
Developing an adaptive sampling approach for rendering performance improvement
to cover the entire buildings at different heights. Extensive experiments have
conducted to verify the effectiveness and efficiency of Aerial-NeRF, and new
state-of-the-art results have been achieved on two public large-scale aerial
datasets and presented SCUTic dataset. Note that our model allows us to perform
rendering over 4 times as fast as compared to multiple competitors. Our
dataset, code, and model are publicly available at https://drliuqi.github.io/.
