  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.
