Leveraging Neural Radiance Fields for Large-Scale 3D Reconstruction from Aerial Imagery

Since conventional photogrammetric approaches struggle with with low-texture, reflective, and transparent regions, this study explores the application of Neural Radiance Fields (NeRFs) for large-scale 3D reconstruction of outdoor scenes, since NeRF-based methods have recently shown very impressive results in these areas. We evaluate three approaches: Mega-NeRF, Block-NeRF, and Direct Voxel Grid Optimization, focusing on their accuracy and completeness compared to ground
truth point clouds. In addition, we analyze the effects of using multiple sub-modules, estimating the visibility by an additional neural network and varying the density threshold for the extraction of the point cloud. For performance valuation, we use benchmark datasets that correspond to the setting off standard flight campaigns and therefore typically have nadir camera perspective and relatively
little image overlap, which can be challenging for NeRF-based approaches that are typically trained
with significantly more images and varying camera angles. We show that despite lower quality
compared to classic photogrammetric approaches, NeRF-based reconstructions provide visually
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convincing results in challenging areas. Furthermore, our study shows that in particular increasing
the number of sub-modules and predicting the visibility using an additional neural network improves
the quality of the resulting reconstructions significantly.