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 \url{https://github.com/fsemerar/satnerf}.

翻译：我们提出了Sat-NeRF——基于最新引入的阴影神经辐射场（S-NeRF）模型的一种改进实现。该方法能够从场景的稀疏卫星图像集合中合成新视角，同时考虑图像中光照变化的影响。训练后的模型还可用于精确估计场景地表高程，这通常是卫星观测应用中的关键需求。S-NeRF通过将辐射度视为反照率与辐照度的函数，对标准神经辐射场（NeRF）方法进行了改进。这两个量均由模型的全连接神经网络分支输出，其中辐照度被定义为太阳直射光与天空漫反射颜色的函数。相关实现基于采用缩放裁剪技术增强的卫星图像数据集运行。针对NeRF的超参数研究揭示了模型收敛性的有趣现象。最终，NeRF与S-NeRF均训练至10万轮次以充分拟合数据并产生最优预测结果。本文相关代码可在\url{https://github.com/fsemerar/satnerf}获取。