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/.

翻译：近期在大规模场景渲染方面的进展，催生了基于神经辐射场（NeRF）的模型，其在小型物体及室内场景合成方面展现出了卓越能力。然而，将该思想扩展至大规模航空渲染面临两个关键问题。首先，单一NeRF无法以高精度渲染复杂大规模航空数据集中的整个场景，因为沿每条视线方向的采样范围不足以充分覆盖建筑物。其次，传统NeRF难以在单个GPU上训练以实现海量图像建模的交互式飞越。相反，现有方法通常将整个场景分割为多个区域，并对每个区域训练一个NeRF，但这类方法难以适应不同的飞行轨迹，且难以实现快速渲染。为此，我们提出Aerial-NeRF，通过三项创新改进联合适配NeRF在大规模航空渲染中的应用：（1）设计基于无人机姿态的自适应空间划分与选择方法，以适配不同飞行轨迹；（2）利用姿态相似性（而非专家网络）加速渲染，以确定新视点所属区域；（3）开发一种自适应采样方法以提升渲染性能，从而覆盖不同高度的完整建筑。大量实验验证了Aerial-NeRF的有效性与高效性，在两个公开大规模航空数据集及我们提出的SCUTic数据集上取得了新的最优结果。值得注意的是，与多个竞争方法相比，我们的模型渲染速度提升超过4倍。我们的数据集、代码及模型已公开于https://drliuqi.github.io/。