Environmental sensing has a major gap: three-dimensional (3D) mapping of the atmosphere over the ocean, under clouds. This gap is caused by occlusion of the domain from satellite views and lack of wide-field 3D sensing from space. Moreover, although water covers most of Earth, almost all sensors are currently on dry land. We address the challenge by deriving a new approach: multi-view imaging using a floating network of cameras that have overlapping view fields. Such a setup introduces new computer vision challenges, which we pose and address. These include effects of water waves on motion-blur, rolling shutter distortion and random tilting. They also include multi-view calibration and geometric recovery in a random environment, using limited embedded resources. Moreover, to cover large areas on Earth, we look into scalability. In conjunction to computer vision, solutions include a new design of drifting floats. We demonstrate a prototype network having two floating nodes, in real sea experiments, yielding information of cloud-base height, with no noticeable rolling shutter or motion blur artifacts.
M.Sc. student under the supervision of Prof. Yoav Schechner.
