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.

在这项研究中，我们介绍了一种专为少量航拍场景渲染量身定制的新方法，即多平面先验引导的神经辐射场模型（MPNeRF）。我们的关键洞察是航拍图像固有的几何规律可以加强稀疏航拍场景的神经辐射场模型。通过研究神经辐射场模型和多平面图像的行为，我们提出使用多平面先验指导神经辐射场模型的训练过程。通过引入 SwinV2 Transformer 和使用 SimMIM 进行预训练，我们的实验结果表明，MPNeRF 在非航拍环境中的性能超过了现有的最先进方法，即使只有三个视图可用，也可以将 SSIM 和 LPIPS 的性能提升三倍。希望我们的工作能为航拍场景中基于神经辐射场模型的应用开发提供启示。