  Neural Radiance Fields (NeRF) have been successfully applied in various
aerial scenes, yet they face challenges with sparse views due to limited
supervision. The acquisition of dense aerial views is often prohibitive, as
unmanned aerial vehicles (UAVs) may encounter constraints in perspective range
and energy constraints. In this work, we introduce Multiplane Prior guided NeRF
(MPNeRF), a novel approach tailored for few-shot aerial scene rendering-marking
a pioneering effort in this domain. Our key insight is that the intrinsic
geometric regularities specific to aerial imagery could be leveraged to enhance
NeRF in sparse aerial scenes. By investigating NeRF's and Multiplane Image
(MPI)'s behavior, we propose to guide the training process of NeRF with a
Multiplane Prior. The proposed Multiplane Prior draws upon MPI's benefits and
incorporates advanced image comprehension through a SwinV2 Transformer,
pre-trained via SimMIM. Our extensive experiments demonstrate that MPNeRF
outperforms existing state-of-the-art methods applied in non-aerial contexts,
by tripling the performance in SSIM and LPIPS even with three views available.
We hope our work offers insights into the development of NeRF-based
applications in aerial scenes with limited data.
