A NeRF for All Seasons
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. Also included in this work is a novel approach to space carving which merges multiple features and consistency metrics
at different spatial scales to create higher quality digital surface map than is possible using standard RGB features.
- Doctor of Philosophy
- Electrical and Computer Engineering
- West Lafayette