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 this https URL

基于 Neural Radiance Fields (NeRF) 的大规模航拍渲染模型 Aerial-NeRF 在适应不同飞行轨迹、提高渲染性能和覆盖整个建筑物等方面进行了三项创新性的改进，通过广泛实验证明了 Aerial-NeRF 的有效性和高效性，并在两个公开的大规模航拍数据集以及 SCUTic 数据集上取得了新的最先进结果，相比其他竞争者，我们的模型能够以 4 倍的速度进行渲染。