We present Sat-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
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介绍了 Sat-NeRF，它是阴影神经辐射场（S-NeRF）模型的一个修改实现。它能够从场景的稀疏卫星图像合成新的视图，并考虑图片中存在的光照变化。通过完全连接的神经网络支路输出反照率和入射辐照度两个量，对辐射度进行考虑，将其作为太阳直射光和天空漫反射颜色的函数。通过对 NeRF 进行超参数研究，得出了一些有趣的结论，并且成功地将 NeRF 和 S-NeRF 运行了 100k 次以完全拟合数据并产生最佳预测。