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

Multi-tiling Neural Radiance Field (NeRF) -- Geometric Assessment on Large-scale Aerial Datasets