
Light-field imaging can be scaled up to a very large area, to map the Earth's atmosphere in 3D. We develop sky integral-imaging, by a wide, scalable network of wide-angle cameras looking upwards, which upload their data to the cloud. This new type of imaging-system poses new computational vision and photography problems, some of which generalize prior monocular tasks. These include radiometric self-calibration across a network, overcoming flare by a network, and background estimation. On the other hand, network redundancy offers solutions to these problems, which we derive. Based on such solutions, the light-field network enables new ways to measure nature. To demonstrate this, we have built the small Sky-Technion Array of Sensors (STARS). This led to experiments showing 3D recovery of clouds, in high spatio-temporal resolution. It is achieved by space carving of the volumetric distribution of semi-transparent clouds. Such sensing can complement satellite imagery, be useful to meteorology, make aerosol tomography realizable, and give new, powerful tools to atmospheric and avian wildlife scientists.
The small Sky-Technion Array of Sensors (STARS) in one experiment
Flare avoidance withour moving parts: static shader for year-round sun trajectories.
The network as a whole has no blind-spot
3D reconstruction of cumulus
A camera unit (node) of the STARS network
Background estimation relaying on network redundancy and cloud-field stationarity
3D reconstruction of altocumulus
Radiometric self-calibration within a network
Auto-tracking extra-terrestrial objects for precise geometric calibration of STARS