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 https://github.gatech.edu/fsemeraro6/satnerf.

翻译：本文提出了Sat-NeRF，一种对近期提出的阴影神经辐射场（S-NeRF）模型的改进实现。该方法能够从场景的稀疏卫星图像集合中合成新视角，同时适应图像中存在的光照变化。训练后的模型还可用于精确估计场景的地表高程，这通常是卫星观测应用中的重要需求。S-NeRF通过将辐射度视为反照率和辐照度的函数，改进了标准神经辐射场（NeRF）方法。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来自太阳的直接光照与来自天空的漫射颜色的函数。该实现使用通过缩放裁剪技术增强的卫星图像数据集进行训练。针对NeRF进行了超参数研究，得出了关于模型收敛性的有趣结论。最后，为了完全拟合数据并产生最佳预测结果，NeRF和S-NeRF均被训练至10万轮次。本文相关代码可访问 https://github.gatech.edu/fsemeraro6/satnerf。