As a result of Shadow NeRF and Sat-NeRF, it is possible to take the solar angle into account in a NeRF-based framework for rendering a scene from a novel viewpoint using satellite images for training. Our work extends those contributions and shows how one can make the renderings season-specific. Our main challenge was creating a Neural Radiance Field (NeRF) that could render seasonal features independently of viewing angle and solar angle while still being able to render shadows. We teach our network to render seasonal features by introducing one more input variable -- time of the year. However, the small training datasets typical of satellite imagery can introduce ambiguities in cases where shadows are present in the same location for every image of a particular season. We add additional terms to the loss function to discourage the network from using seasonal features for accounting for shadows. We show the performance of our network on eight Areas of Interest containing images captured by the Maxar WorldView-3 satellite. This evaluation includes tests measuring the ability of our framework to accurately render novel views, generate height maps, predict shadows, and specify seasonal features independently from shadows. Our ablation studies justify the choices made for network design parameters.

我们的研究工作旨在通过引入射影纹理泛函对影子进行处理，实现在用于训练的卫星图像的基础上，考虑太阳角度在渲染场景时的影响，同时使得渲染的结果与季节特征和阴影无关。通过对八个感兴趣区域的图像进行评估，我们的网络表现出了准确渲染新视角、生成高程图、预测阴影和独立识别季节特征的能力，我们的消融研究为网络设计参数的选择提供了理论依据。

利用卫星图像将季节和太阳特定性纳入 NeRF 架构生成的渲染