Cloudmaps from static ground-view video

Abstract

Cloud shadows dramatically affect the appearance of outdoor scenes. We describe three approaches that use video of cloud shadows to estimate a cloudmap, a spatio-temporal function that represents the clouds passing over the scene. Two of the methods make assumptions about the camera and/or scene geometry. The third method uses techniques from manifold learning and does not require such assumptions. None of the methods require directly viewing the clouds, but instead use the pattern of intensity changes caused by the cloud shadows. An accurate estimate of the cloudmap has potential applications in solar power estimation and forecasting, surveillance, and graphics. We present a quantitative evaluation of our methods on synthetic scenes and show qualitative results on real scenes. We also demonstrate the use of a cloudmap for foreground object detection and video editing.