  We present Surf-NeRF, a modified implementation of the recently introduced
Shadow Neural Radiance Field (S-NeRF) model. This method is able to synthesize
novel views from a sparse set of satellite images of a scene, while accounting
for the variation in lighting present in the pictures. The trained model can
also be used to accurately estimate the surface elevation of the scene, which
is often a desirable quantity for satellite observation applications. S-NeRF
improves on the standard Neural Radiance Field (NeRF) method by considering the
radiance as a function of the albedo and the irradiance. Both these quantities
are output by fully connected neural network branches of the model, and the
latter is considered as a function of the direct light from the sun and the
diffuse color from the sky. The implementations were run on a dataset of
satellite images, augmented using a zoom-and-crop technique. A hyperparameter
study for NeRF was carried out, leading to intriguing observations on the
model's convergence. Finally, both NeRF and S-NeRF were run until 100k epochs
in order to fully fit the data and produce their best possible predictions. The
code related to this article can be found at
https://github.com/fsemerar/surfnerf.
