Neural Radiance Fields (NeRF) offer the potential to benefit 3D
reconstruction tasks, including aerial photogrammetry. However, the scalability
and accuracy of the inferred geometry are not well-documented for large-scale
aerial assets,since such datasets usually result in very high memory
consumption and slow convergence.. In this paper, we aim to scale the NeRF on
large-scael aerial datasets and provide a thorough geometry assessment of NeRF.
Specifically, we introduce a location-specific sampling technique as well as a
multi-camera tiling (MCT) strategy to reduce memory consumption during image
loading for RAM, representation training for GPU memory, and increase the
convergence rate within tiles. MCT decomposes a large-frame image into multiple
tiled images with different camera models, allowing these small-frame images to
be fed into the training process as needed for specific locations without a
loss of accuracy. We implement our method on a representative approach,
Mip-NeRF, and compare its geometry performance with threephotgrammetric MVS
pipelines on two typical aerial datasets against LiDAR reference data. Both
qualitative and quantitative results suggest that the proposed NeRF approach
produces better completeness and object details than traditional approaches,
although as of now, it still falls short in terms of accuracy.

通过增加位置特定的采样技术和多镜头分块策略，我们在大规模航拍数据集上扩展了 Neural Radiance Fields (NeRF) 模型，并对 NeRF 的几何特性进行了全面评估。我们的方法 Mip-NeRF 在几何性能上相较于传统方法具有更好的完整性和物体细节，但目前仍然在准确度方面存在不足。