  We present a neural radiance field method for urban-scale semantic and
building-level instance segmentation from aerial images by lifting noisy 2D
labels to 3D. This is a challenging problem due to two primary reasons.
Firstly, objects in urban aerial images exhibit substantial variations in size,
including buildings, cars, and roads, which pose a significant challenge for
accurate 2D segmentation. Secondly, the 2D labels generated by existing
segmentation methods suffer from the multi-view inconsistency problem,
especially in the case of aerial images, where each image captures only a small
portion of the entire scene. To overcome these limitations, we first introduce
a scale-adaptive semantic label fusion strategy that enhances the segmentation
of objects of varying sizes by combining labels predicted from different
altitudes, harnessing the novel-view synthesis capabilities of NeRF. We then
introduce a novel cross-view instance label grouping strategy based on the 3D
scene representation to mitigate the multi-view inconsistency problem in the 2D
instance labels. Furthermore, we exploit multi-view reconstructed depth priors
to improve the geometric quality of the reconstructed radiance field, resulting
in enhanced segmentation results. Experiments on multiple real-world
urban-scale datasets demonstrate that our approach outperforms existing
methods, highlighting its effectiveness.
